@inproceedings{MinhICML2020,
    title={Optimizing Dynamic Structures with Bayesian Generative Search},
    author = {M. Hoang and C. Kingsford},
    booktitle={Internation Conference on Machine Learning},
    year={2020}
}
@article{he2018nais,
  title={Nais: Neural attentive item similarity model for recommendation},
  author={He, Xiangnan and He, Zhankui and Song, Jingkuan and Liu, Zhenguang and Jiang, Yu-Gang and Chua, Tat-Seng},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={30},
  number={12},
  pages={2354--2366},
  year={2018},
  publisher={IEEE}
}
@inproceedings{xie2021federated,
  author       = {Han Xie and
                  Jing Ma and
                  Li Xiong and
                  Carl Yang},
  editor       = {Marc'Aurelio Ranzato and
                  Alina Beygelzimer and
                  Yann N. Dauphin and
                  Percy Liang and
                  Jennifer Wortman Vaughan},
  title        = {Federated Graph Classification over Non-IID Graphs},
  booktitle    = {Advances in Neural Information Processing Systems 34: Annual Conference
                  on Neural Information Processing Systems 2021, NeurIPS 2021, December
                  6-14, 2021, virtual},
}
@inproceedings{lee2018collaborative,
  title={Collaborative deep metric learning for video understanding},
  author={Lee, Joonseok and Abu-El-Haija, Sami and Varadarajan, Balakrishnan and Natsev, Apostol},
  booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={481--490},
  year={2018}
}
@inproceedings{Yehong17,
  title={Information-based multi-fidelity Bayesian optimization},
  author={Y. Zhang and T. N. Hoang and K. H. Low and M. Kankanhalli},
  booktitle={NIPS Workshop BayesOpt},
  year={2017}
}

@article{Gal15,
  title={Improving the Gaussian process sparse spectrum approximation by representing uncertainty in frequency inputs},
  author={Gal, Yarin and Turner, Richard},
  year={2015},
  publisher={Microtome Publishing}
}
@article{Kapralov19,
  title={Oblivious sketching of high-degree polynomial kernels},
  author={Kapralov, Michael and Pagh, Rasmus and Velingker, Ameya and Woodruff, David and Zandieh, Amir},
  journal={arXiv preprint arXiv:1909.01410},
  year={2019}
}

@inproceedings{Yee19,
  title={Disentangling Disentanglement in Variational Autoencoders},
  author={Emile Mathieu and Tom Rainforth and Siddharth Narayanaswamy and Yee Whye Teh},
  booktitle={ICML},
  year={2019}
}

@incollection{Kingma15,
title = {Variational Dropout and the Local Reparameterization Trick},
author = {Kingma, Durk P and Salimans, Tim and Welling, Max},
booktitle = {Advances in Neural Information Processing Systems 28},
editor = {C. Cortes and N. D. Lawrence and D. D. Lee and M. Sugiyama and R. Garnett},
pages = {2575--2583},
year = {2015},
publisher = {Curran Associates, Inc.}
}

@inproceedings{kipf2016semi,
  title={Semi-Supervised Classification with Graph Convolutional Networks},
  author={Kipf, Thomas N and Welling, Max},
  booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/pdf?id=SJU4ayYgl}
}

@inproceedings{
chen2018fastgcn,
title={Fast{GCN}: Fast Learning with Graph Convolutional Networks via Importance Sampling},
author={Jie Chen and Tengfei Ma and Cao Xiao},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=rytstxWAW},
}

@inproceedings{DBLP:journals/corr/HamiltonYL17,
  author       = {William L. Hamilton and
                  Zhitao Ying and
                  Jure Leskovec},
  editor       = {Isabelle Guyon and
                  Ulrike von Luxburg and
                  Samy Bengio and
                  Hanna M. Wallach and
                  Rob Fergus and
                  S. V. N. Vishwanathan and
                  Roman Garnett},
  title        = {Inductive Representation Learning on Large Graphs},
  year = {2017},
  booktitle    = {Annual Conference
                  on Neural Information Processing Systems 2017},
}


@article{DBLP:journals/corr/DefferrardBV16,
  author    = {Micha{\"{e}}l Defferrard and
               Xavier Bresson and
               Pierre Vandergheynst},
  title     = {Convolutional Neural Networks on Graphs with Fast Localized Spectral
               Filtering},
  journal   = {CoRR},
  volume    = {abs/1606.09375},
  year      = {2016}
}

@article{graves2016hybrid,
  title={Hybrid computing using a neural network with dynamic external memory},
  author={Graves, Alex and Wayne, Greg and Reynolds, Malcolm and Harley, Tim and Danihelka, Ivo and Grabska-Barwi{\'n}ska, Agnieszka and Colmenarejo, Sergio G{\'o}mez and Grefenstette, Edward and Ramalho, Tiago and Agapiou, John and others},
  journal={Nature},
  volume={538},
  number={7626},
  pages={471},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{Zitnik2018,
  title     = {Modeling polypharmacy side effects with graph convolutional networks.},
  author    = {Zitnik, Marinka and Agrawal, Monica and Leskovec, Jure},
  journal   = {Bioinformatics},
  volume    = {34},
  number    = {13},
  pages     = {457–466},
  year      = {2018}
}

@article{DBLP:journals/corr/abs-1804-10850,
  author    = {Tengfei Ma and
               Cao Xiao and
               Jiayu Zhou and
               Fei Wang},
  title     = {Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders},
  journal   = {CoRR},
  volume    = {abs/1804.10850},
  year      = {2018},
  url       = {http://arxiv.org/abs/1804.10850},
  archivePrefix = {arXiv}
}

@article{pytorch,
  title={Automatic differentiation in PyTorch},
  author={Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and DeVito, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam},
  year={2017}
}

@article{DBLP:journals/corr/KingmaB14,
  author    = {Diederik P. Kingma and
               Jimmy Ba},
  title     = {Adam: {A} Method for Stochastic Optimization},
  journal   = {CoRR},
  volume    = {abs/1412.6980},
  year      = {2014},
  url       = {http://arxiv.org/abs/1412.6980},
  archivePrefix = {arXiv},
  eprint    = {1412.6980},
  timestamp = {Mon, 13 Aug 2018 16:47:35 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/KingmaB14},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}



@article{Tatonetti2012,
  author    = {Tatonetti, NP. and Ye, PP. and Daneshjou, R. and Altman, RB.},
  title     = {Data-Driven Prediction of Drug Effects and Interactions.},
  journal   = {Science Translational Medicine},
  volume    = {4(125)},
  year      = {2012}
}

@article{doi:10.1093/jamia/ocy068,
author = {Xiao, Cao and Choi, Edward and Sun, Jimeng},
title = {Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review},
journal = {Journal of the American Medical Informatics},
year = {2018}
}

@article{tatonetti2012data,
  title={Data-driven prediction of drug effects and interactions},
  author={Tatonetti, NP. and Patrick, P. and Daneshjou, R. and Altman, RB.},
  journal={Science translational medicine},
  volume={4},
  number={125},
  pages={125ra31--125ra31},
  year={2012}
}

@inproceedings{DBLP:journals/corr/WestonCB14,
  title     = {Memory Networks},
  author    = {Jason Weston and
               Sumit Chopra and
               Antoine Bordes},
  booktitle={International Conference on Learning Representations},
  year={2015}
}

@inproceedings{sukhbaatar2015end,
  title={End-to-end memory networks},
  author={Sukhbaatar, Sainbayar and Weston, Jason and Fergus, Rob and others},
  booktitle={NeurIPS},
  pages={2440--2448},
  year={2015}
}

@inproceedings{kumar2016ask,
  title={Ask me anything: Dynamic memory networks for natural language processing},
  author={Kumar, Ankit and Irsoy, Ozan and Ondruska, Peter and Iyyer, Mohit and Bradbury, James and Gulrajani, Ishaan and Zhong, Victor and Paulus, Romain and Socher, Richard},
  booktitle={International Conference on Machine Learning},
  pages={1378--1387},
  year={2016}
}

@inproceedings{miller2016key,
  title={Key-value memory networks for directly reading documents},
  author={Miller, Alexander and Fisch, Adam and Dodge, Jesse and Karimi, Amir-Hossein and Bordes, Antoine and Weston, Jason},
  booktitle={Empirical Methods in Natural Language Processing},
  pages={1400--1409},
  year={2016}
}


@article{johnson2016mimic,
  title={MIMIC-III, a freely accessible critical care database},
  author={Johnson, Alistair EW and Pollard, Tom J and Shen, Lu and Li-wei, H Lehman and Feng, Mengling and Ghassemi, Mohammad and Moody, Benjamin and Szolovits, Peter and Celi, Leo Anthony and Mark, Roger G},
  journal={Scientific data},
  volume={3},
  pages={160035},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{ma2018drug,
  title={Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders},
  author={Ma, Tengfei and Xiao, Cao and Zhou, Jiayu and Wang, Fei},
  journal={arXiv preprint arXiv:1804.10850},
  year={2018}
}

@inproceedings{ma2017dipole,
  title={Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks},
  author={Ma, Fenglong and Chitta, Radha and Zhou, Jing and You, Quanzeng and Sun, Tong and Gao, Jing},
  booktitle={KDD},
  pages={1903--1911},
  year={2017},
  organization={ACM}
}
@inproceedings{pham2016deepcare,
  title={Deepcare: A deep dynamic memory model for predictive medicine},
  author={Pham, Trang and Tran, Truyen and Phung, Dinh and Venkatesh, Svetha},
  booktitle={PA-KDD},
  pages={30--41},
  year={2016},
  organization={Springer}
}

@inproceedings{choi2016doctor,
  title={Doctor ai: Predicting clinical events via recurrent neural networks},
  author={Choi, Edward and Bahadori, Mohammad Taha and Schuetz, Andy and Stewart, Walter F and Sun, Jimeng},
  booktitle={MLHC},
  pages={301--318},
  year={2016}
}

@article{lipton2015learning,
  title={Learning to diagnose with LSTM recurrent neural networks},
  author={Lipton, Zachary C and Kale, David C and Elkan, Charles and Wetzel, Randall},
  journal={arXiv preprint arXiv:1511.03677},
  year={2015}
}

@inproceedings{esteban2016predicting,
  title={Predicting clinical events by combining static and dynamic information using recurrent neural networks},
  author={Esteban, Crist{\'o}bal and Staeck, Oliver and Baier, Stephan and Yang, Yinchong and Tresp, Volker},
  booktitle={ICHI},
  pages={93--101},
  year={2016},
  organization={IEEE}
}

@inproceedings{jagannatha2016bidirectional,
  title={Bidirectional RNN for medical event detection in electronic health records},
  author={Jagannatha, Abhyuday N and Yu, Hong},
  booktitle={NAACL},
  volume={2016},
  pages={473},
  year={2016},
  organization={NIH Public Access}
}

@article{miotto2016deep,
  title={Deep patient: an unsupervised representation to predict the future of patients from the electronic health records},
  author={Miotto, Riccardo and Li, Li and Kidd, Brian A and Dudley, Joel T},
  journal={Scientific reports},
  volume={6},
  pages={26094},
  year={2016}
}

@article{choi2016medical,
  title={Medical concept representation learning from electronic health records and its application on heart failure prediction},
  author={Choi, Edward and Schuetz, Andy and Stewart, Walter F and Sun, Jimeng},
  journal={arXiv preprint arXiv:1602.03686},
  year={2016}
}

@article{nguyen2017mathtt,
  title={Deepr: A Convolutional Net for Medical Records},
  author={Nguyen, Phuoc and Tran, Truyen and Wickramasinghe, Nilmini and Venkatesh, Svetha},
  journal={IEEE journal of biomedical and health informatics},
  volume={21},
  number={1},
  pages={22--30},
  year={2017},
  publisher={IEEE}
}

@inproceedings{zhang2017leap,
  title={Leap: Learning to prescribe effective and safe treatment combinations for multimorbidity},
  author={Zhang, Yutao and Chen, Robert and Tang, Jie and Stewart, Walter F and Sun, Jimeng},
  booktitle={Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={1315--1324},
  year={2017},
  organization={ACM}
}

@article{wang2017safe,
  title={Safe Medicine Recommendation via Medical Knowledge Graph Embedding},
  author={Wang, Meng and Liu, Mengyue and Liu, Jun and Wang, Sen and Long, Guodong and Qian, Buyue},
  journal={arXiv preprint arXiv:1710.05980},
  year={2017}
}

@inproceedings{le2018dual,
  title={Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning},
  author={Le, Hung and Tran, Truyen and Venkatesh, Svetha},
  booktitle={Proceedings of the 24rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={1637--1645},
  year={2018},
  organization={ACM}
}

@article{schriml2011disease,
  title={Disease Ontology: a backbone for disease semantic integration},
  author={Schriml, Lynn Marie and Arze, Cesar and Nadendla, Suvarna and Chang, Yu-Wei Wayne and Mazaitis, Mark and Felix, Victor and Feng, Gang and Kibbe, Warren Alden},
  journal={Nucleic acids research},
  volume={40},
  number={D1},
  pages={D940--D946},
  year={2011},
  publisher={Oxford University Press}
}

@article{law2013drugbank,
  title={DrugBank 4.0: shedding new light on drug metabolism},
  author={Law, Vivian and Knox, Craig and Djoumbou, Yannick and Jewison, Tim and Guo, An Chi and Liu, Yifeng and Maciejewski, Adam and Arndt, David and Wilson, Michael and Neveu, Vanessa and others},
  journal={Nucleic acids research},
  volume={42},
  number={D1},
  pages={D1091--D1097},
  year={2013},
  publisher={Oxford University Press}
}


@article{almirall2012designing,
  title={Designing a pilot sequential multiple assignment randomized trial for developing an adaptive treatment strategy},
  author={Almirall, Daniel and Compton, Scott N and Gunlicks-Stoessel, Meredith and Duan, Naihua and Murphy, Susan A},
  journal={Statistics in medicine},
  volume={31},
  number={17},
  pages={1887--1902},
  year={2012},
  publisher={Wiley Online Library}
}

@article{gunlicks2016pilot,
  title={A pilot SMART for developing an adaptive treatment strategy for adolescent depression},
  author={Gunlicks-Stoessel, Meredith and Mufson, Laura and Westervelt, Ana and Almirall, Daniel and Murphy, Susan},
  journal={Journal of Clinical Child \& Adolescent Psychology},
  volume={45},
  number={4},
  pages={480--494},
  year={2016},
  publisher={Taylor \& Francis}
}

@inproceedings{choi2017gram,
  title={GRAM: Graph-based attention model for healthcare representation learning},
  author={Choi, Edward and Bahadori, Mohammad Taha and Song, Le and Stewart, Walter F and Sun, Jimeng},
  booktitle={KDD},
  pages={787--795},
  year={2017}
}

@inproceedings{choi2016retain,
  title={Retain: An interpretable predictive model for healthcare using reverse time attention mechanism},
  author={Choi, Edward and Bahadori, Mohammad Taha and Sun, Jimeng and Kulas, Joshua and Schuetz, Andy and Stewart, Walter},
  booktitle={NeurIPS},
  pages={3504--3512},
  year={2016}
}

@inproceedings{sutskever2014sequence,
  title={Sequence to sequence learning with neural networks},
  author={Sutskever, Ilya and Vinyals, Oriol and Le, Quoc V},
  booktitle={NeurIPS},
  pages={3104--3112},
  year={2014}
}

@article{luaces2012binary,
  title={Binary relevance efficacy for multilabel classification},
  author={Luaces, Oscar and D{\'\i}ez, Jorge and Barranquero, Jos{\'e} and del Coz, Juan Jos{\'e} and Bahamonde, Antonio},
  journal={Progress in Artificial Intelligence},
  volume={1},
  number={4},
  pages={303--313},
  year={2012},
  publisher={Springer}
}

@article{cho2014properties,
  title={On the properties of neural machine translation: Encoder-decoder approaches},
  author={Cho, Kyunghyun and Van Merri{\"e}nboer, Bart and Bahdanau, Dzmitry and Bengio, Yoshua},
  journal={arXiv preprint arXiv:1409.1259},
  year={2014}
}

@article{chung2014empirical,
  title={Empirical evaluation of gated recurrent neural networks on sequence modeling},
  author={Chung, Junyoung and Gulcehre, Caglar and Cho, KyungHyun and Bengio, Yoshua},
  journal={arXiv preprint arXiv:1412.3555},
  year={2014}
}

@article{hochreiter1997long,
  title={Long short-term memory},
  author={Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
  journal={Neural computation},
  volume={9},
  number={8},
  pages={1735--1780},
  year={1997},
  publisher={MIT Press}
}

@article{zhang2014review,
  title={A review on multi-label learning algorithms},
  author={Zhang, Min-Ling and Zhou, Zhi-Hua},
  journal={IEEE transactions on knowledge and data engineering},
  volume={26},
  number={8},
  pages={1819--1837},
  year={2014},
  publisher={IEEE}
}

@inproceedings{lin2015learning,
  title={Learning entity and relation embeddings for knowledge graph completion.},
  author={Lin, Yankai and Liu, Zhiyuan and Sun, Maosong and Liu, Yang and Zhu, Xuan},
  booktitle={AAAI},
  volume={15},
  pages={2181--2187},
  year={2015}
}

@article{luaces2012binary,
  title={Binary relevance efficacy for multilabel classification},
  author={Luaces, Oscar and D{\'\i}ez, Jorge and Barranquero, Jos{\'e} and del Coz, Juan Jos{\'e} and Bahamonde, Antonio},
  journal={Progress in Artificial Intelligence},
  volume={1},
  number={4},
  pages={303--313},
  year={2012},
  publisher={Springer}
}

@article{kirkpatrick1983optimization,
  title={Optimization by simulated annealing},
  author={Kirkpatrick, Scott and Gelatt, C Daniel and Vecchi, Mario P},
  journal={science},
  volume={220},
  number={4598},
  pages={671--680},
  year={1983},
  publisher={American Association for the Advancement of Science}
}

@article{srivastava2014dropout,
  title={Dropout: a simple way to prevent neural networks from overfitting},
  author={Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
  journal={The Journal of Machine Learning Research},
  volume={15},
  number={1},
  pages={1929--1958},
  year={2014},
  publisher={JMLR. org}
}

@inproceedings{hu2017toward,
  title={Toward controlled generation of text},
  author={Hu, Zhiting and Yang, Zichao and Liang, Xiaodan and Salakhutdinov, Ruslan and Xing, Eric P},
  booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70},
  pages={1587--1596},
  year={2017},
  organization={JMLR. org}
}

@inproceedings{shen2017style,
  title={Style transfer from non-parallel text by cross-alignment},
  author={Shen, Tianxiao and Lei, Tao and Barzilay, Regina and Jaakkola, Tommi},
  booktitle={NeurIPS},
  pages={6830--6841},
  year={2017}
}

@inproceedings{yu2017seqgan,
  title={Seqgan: Sequence generative adversarial nets with policy gradient},
  author={Yu, Lantao and Zhang, Weinan and Wang, Jun and Yu, Yong},
  booktitle={Thirty-First AAAI Conference on Artificial Intelligence},
  year={2017}
}

@inproceedings{lamb2016professor,
  title={Professor forcing: A new algorithm for training recurrent networks},
  author={Lamb, Alex M and Goyal, Anirudh Goyal Alias Parth and Zhang, Ying and Zhang, Saizheng and Courville, Aaron C and Bengio, Yoshua},
  booktitle={NeurIPS},
  pages={4601--4609},
  year={2016}
}

@article{li2018generative,
  title={A generative model for category text generation},
  author={Li, Yang and Pan, Quan and Wang, Suhang and Yang, Tao and Cambria, Erik},
  journal={Information Sciences},
  volume={450},
  pages={301--315},
  year={2018},
  publisher={Elsevier}
}

@misc{shire2013rare,
  title={Rare Disease Impact Report: Insights from patients and the medical community},
  author={Shire, HGT},
  year={2013},
  publisher={Shire}
}

@inproceedings{Tang:2019:RPI:3292500.3330907,
 author = {Tang, Fengyi and Xiao, Cao and Wang, Fei and Zhou, Jiayu and Lehman, Li-wei H.},
 title = {Retaining Privileged Information for Multi-Task Learning},
 booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
 series = {KDD '19},
 year = {2019},
 isbn = {978-1-4503-6201-6},
 location = {Anchorage, AK, USA},
 pages = {1369--1377},
 numpages = {9},
 url = {http://doi.acm.org/10.1145/3292500.3330907},
 doi = {10.1145/3292500.3330907},
 acmid = {3330907},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {electronic health records, multi-task learning, privileged information},
} 

@inproceedings{choi2018mime,
  title={Mime: Multilevel medical embedding of electronic health records for predictive healthcare},
  author={Choi, Edward and Xiao, Cao and Stewart, Walter and Sun, Jimeng},
  booktitle={NeurIPS},
  pages={4547--4557},
  year={2018}
}

@misc{cui2019conan,
    title={CONAN: Complementary Pattern Augmentation for Rare Disease Detection},
    author={Limeng Cui and Siddharth Biswal and Lucas M. Glass and Greg Lever and Jimeng Sun and Cao Xiao},
    year={2019},
    eprint={1911.13232},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

@article{zhang2019metapred,
  title={MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records},
  author={Zhang, Xi Sheryl and Tang, Fengyi and Dodge, Hiroko and Zhou, Jiayu and Wang, Fei},
  journal={arXiv preprint arXiv:1905.03218},
  year={2019}
}


@inbook{doi:10.1137/1.9781611974973.23,
author = {Chao Che and Cao Xiao and Jian Liang and Bo Jin and Jiayu Zho and Fei Wang},
title = {An RNN Architecture with Dynamic Temporal Matching for Personalized Predictions of Parkinson's Disease},
booktitle = {Proceedings of the 2017 SIAM International Conference on Data Mining},
chapter = {},
pages = {198-206},
year = {2017}
}


@article{Meltzer2008,
  title={Idiopathic pulmonary fibrosis},
  author={Meltzer, Eric and Noble, Paul},
  journal={Orphanet Journal of Rare Diseases},
  year={2008}
}

@article{choi2016learning,
  title={Learning low-dimensional representations of medical concepts},
  author={Choi, Youngduck and Chiu, Chill Yi-I and Sontag, David},
  journal={AMIA Summits on Translational Science Proceedings},
  year={2016}
}



@article{Vickers19,
  title={Challenges and Opportunities In The Treatment Of Rare Diseases},
  author={Vickers, Philip},
  journal={Drug Discovery World},
  year={2019}
}

@inproceedings{baytas2017patient,
  title={Patient subtyping via time-aware LSTM networks},
  author={Baytas, Inci M and Xiao, Cao and Zhang, Xi and Wang, Fei and Jain, Anil K and Zhou, Jiayu},
  booktitle={KDD},
  pages={65--74},
  year={2017}
}

@inproceedings{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  booktitle={NeurIPS},
  pages={5998--6008},
  year={2017}
}

@inproceedings{Zhang2018SelfAttentionGA,
  title={Self-Attention Generative Adversarial Networks},
  author={Han Zhang and Ian J. Goodfellow and Dimitris N. Metaxas and Augustus Odena},
  booktitle={ICML},
  year={2018}
}

@inproceedings{ilse2018attention,
  title={Attention-based Deep Multiple Instance Learning},
  author={Ilse, Maximilian and Tomczak, Jakub and Welling, Max},
  booktitle={ICML},
  pages={2132--2141},
  year={2018}
}

@inproceedings{yang2016hierarchical,
  title={Hierarchical attention networks for document classification},
  author={Yang, Zichao and Yang, Diyi and Dyer, Chris and He, Xiaodong and Smola, Alex and Hovy, Eduard},
  booktitle={NAACL-HLT},
  pages={1480--1489},
  year={2016}
}

@inproceedings{yang2016stacked,
  title={Stacked attention networks for image question answering},
  author={Yang, Zichao and He, Xiaodong and Gao, Jianfeng and Deng, Li and Smola, Alex},
  booktitle={CVPR},
  pages={21--29},
  year={2016}
}

@article{bahdanau2014neural,
  title={Neural machine translation by jointly learning to align and translate},
  author={Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua},
  journal={arXiv preprint arXiv:1409.0473},
  year={2014}
}

@inproceedings{luong2015effective,
  title={Effective Approaches to Attention-based Neural Machine Translation},
  author={Luong, Thang and Pham, Hieu and Manning, Christopher D},
  booktitle={EMNLP},
  pages={1412--1421},
  year={2015}
}

@article{lin2017structured,
  title={A structured self-attentive sentence embedding},
  author={Lin, Zhouhan and Feng, Minwei and Santos, Cicero Nogueira dos and Yu, Mo and Xiang, Bing and Zhou, Bowen and Bengio, Yoshua},
  journal={arXiv preprint arXiv:1703.03130},
  year={2017}
}

@inproceedings{tan2018deep,
  title={Deep semantic role labeling with self-attention},
  author={Tan, Zhixing and Wang, Mingxuan and Xie, Jun and Chen, Yidong and Shi, Xiaodong},
  booktitle={AAAI},
  year={2018}
}

@inproceedings{verga2018simultaneously,
  title={Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction},
  author={Verga, Patrick and Strubell, Emma and McCallum, Andrew},
  booktitle={NAACL-HLT},
  pages={872--884},
  year={2018}
}

@inproceedings{choi2017generating,
  title={Generating Multi-label Discrete Patient Records using Generative Adversarial Networks},
  author={Choi, Edward and Biswal, Siddharth and Malin, Bradley and Duke, Jon and Stewart, Walter F and Sun, Jimeng},
  booktitle={MLHC},
  pages={286--305},
  year={2017}
}

@inproceedings{ma2018risk,
  title={Risk prediction on electronic health records with prior medical knowledge},
  author={Ma, Fenglong and Gao, Jing and Suo, Qiuling and You, Quanzeng and Zhou, Jing and Zhang, Aidong},
  booktitle={KDD},
  pages={1910--1919},
  year={2018}
}

@inproceedings{ma2018kame,
  title={Kame: Knowledge-based attention model for diagnosis prediction in healthcare},
  author={Ma, Fenglong and You, Quanzeng and Xiao, Houping and Chitta, Radha and Zhou, Jing and Gao, Jing},
  booktitle={CIKM},
  pages={743--752},
  year={2018},
  organization={ACM}
}

@inproceedings{che2017boosting,
  title={Boosting deep learning risk prediction with generative adversarial networks for electronic health records},
  author={Che, Zhengping and Cheng, Yu and Zhai, Shuangfei and Sun, Zhaonan and Liu, Yan},
  booktitle={ICDM},
  pages={787--792},
  year={2017},
  organization={IEEE}
}

@inproceedings{lin2017focal,
  title={Focal loss for dense object detection},
  author={Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
  booktitle={ICCV},
  pages={2980--2988},
  year={2017}
}

@article{yu2019rare,
  title={Rare Disease Detection by Sequence Modeling with Generative Adversarial Networks},
  author={Yu, Kezi and Wang, Yunlong and Cai, Yong and Xiao, Cao and Zhao, Emily and Glass, Lucas and Sun, Jimeng},
  journal={arXiv preprint arXiv:1907.01022},
  year={2019}
}

@inproceedings{mclachlan2016using,
  title={Using the caremap with health incidents statistics for generating the realistic synthetic electronic healthcare record},
  author={McLachlan, Scott and Dube, Kudakwashe and Gallagher, Thomas},
  booktitle={ICHI},
  pages={439--448},
  year={2016}
}

@article{ley2017code,
  title={Code-based diagnostic algorithms for idiopathic pulmonary fibrosis. Case validation and improvement},
  author={Ley, Brett and Urbania, Thomas and Husson, Gail and Vittinghoff, Eric and Brush, David R and Eisner, Mark D and Iribarren, Carlos and Collard, Harold R},
  journal={Annals of the American Thoracic Society},
  volume={14},
  number={6},
  pages={880--887},
  year={2017},
  publisher={American Thoracic Society}
}

@inproceedings{dai2010minimum,
  title={Minimum conditional entropy clustering: A discriminative framework for clustering},
  author={Dai, Bo and Hu, Baogang},
  booktitle={ACML},
  pages={47--62},
  year={2010}
}

@article{kreuter2014management,
  title={Management of idiopathic pulmonary fibrosis: selected case reports},
  author={Kreuter, Michael and Kardos, Peter and Hoffstein, Victor},
  journal={European Respiratory Review},
  volume={23},
  number={132},
  pages={239--248},
  year={2014},
  publisher={Eur Respiratory Soc}
}

@article{cordeiro2013clinical,
  title={Clinical case: Differential diagnosis of idiopathic pulmonary fibrosis},
  author={Cordeiro, Carlos Robalo and Alfaro, Tiago M and Freitas, Sara},
  journal={BMC research notes},
  volume={6},
  number={1},
  pages={S1},
  year={2013},
  publisher={BioMed Central}
}

@inproceedings{zhang2017stackgan,
  title={Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks},
  author={Zhang, Han and Xu, Tao and Li, Hongsheng and Zhang, Shaoting and Wang, Xiaogang and Huang, Xiaolei and Metaxas, Dimitris N},
  booktitle={ICCV},
  pages={5907--5915},
  year={2017}
}

@incollection{NIPS2014_5423,
title = {Generative Adversarial Nets},
author = {Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
booktitle = {NeurIPS},
pages = {2672--2680},
year = {2014}
}


@inproceedings{reed2016generative,
  title={Generative Adversarial Text to Image Synthesis},
  author={Reed, Scott and Akata, Zeynep and Yan, Xinchen and Logeswaran, Lajanugen and Schiele, Bernt and Lee, Honglak},
  booktitle={ICML},
  pages={1060--1069},
  year={2016}
}

@inproceedings{reed2016learning,
  title={Learning what and where to draw},
  author={Reed, Scott E and Akata, Zeynep and Mohan, Santosh and Tenka, Samuel and Schiele, Bernt and Lee, Honglak},
  booktitle={NeurIPS},
  pages={217--225},
  year={2016}
}

@inproceedings{ledig2017photo,
  title={Photo-realistic single image super-resolution using a generative adversarial network},
  author={Ledig, Christian and Theis, Lucas and Husz{\'a}r, Ferenc and Caballero, Jose and Cunningham, Andrew and Acosta, Alejandro and Aitken, Andrew and Tejani, Alykhan and Totz, Johannes and Wang, Zehan and others},
  booktitle={CVPR},
  pages={4681--4690},
  year={2017}
}

@inproceedings{vasu2018analyzing,
  title={Analyzing perception-distortion tradeoff using enhanced perceptual super-resolution network},
  author={Vasu, Subeesh and Thekke Madam, Nimisha and Rajagopalan, AN},
  booktitle={ECCV},
  year={2018}
}

@article{brock2018large,
  title={Large scale gan training for high fidelity natural image synthesis},
  author={Brock, Andrew and Donahue, Jeff and Simonyan, Karen},
  journal={arXiv preprint arXiv:1809.11096},
  year={2018}
}

@article{hinton2012neural,
  title={Neural networks for machine learning lecture 6a overview of mini-batch gradient descent},
  author={Hinton, Geoffrey and Srivastava, Nitish and Swersky, Kevin},
  volume={14},
  pages={8},
  year={2012}
}

@article{maaten2008visualizing,
  title={Visualizing data using t-SNE},
  author={Maaten, Laurens van der and Hinton, Geoffrey},
  journal={JMLR},
  volume={9},
  number={Nov},
  pages={2579--2605},
  year={2008}
}

@inproceedings{elkan2008learning,
  title={Learning classifiers from only positive and unlabeled data},
  author={Elkan, Charles and Noto, Keith},
  booktitle={KDD},
  pages={213--220},
  year={2008}
}

@inproceedings{kiryo2017positive,
  title={Positive-unlabeled learning with non-negative risk estimator},
  author={Kiryo, Ryuichi and Niu, Gang and du Plessis, Marthinus C and Sugiyama, Masashi},
  booktitle={NeurIPS},
  pages={1675--1685},
  year={2017}
}

@inproceedings{liu2003building,
  title={Building Text Classifiers Using Positive and Unlabeled Examples.},
  author={Liu, Bing and Dai, Yang and Li, Xiaoli and Lee, Wee Sun and Philip, S Yu},
  booktitle={ICDM},
  volume={3},
  pages={179--188},
  year={2003},
  organization={Citeseer}
}

@article{bekker2018learning,
  title={Learning from positive and unlabeled data: A survey},
  author={Bekker, Jessa and Davis, Jesse},
  journal={arXiv preprint arXiv:1811.04820},
  year={2018}
}

@inproceedings{li2010negative,
  title={Negative training data can be harmful to text classification},
  author={Li, Xiao-Li and Liu, Bing and Ng, See-Kiong},
  booktitle={EMNLP},
  pages={218--228},
  year={2010}
}

@inproceedings{li2007learning,
  title={Learning to Identify Unexpected Instances in the Test Set.},
  author={Li, Xiaoli and Liu, Bing and Ng, See-Kiong},
  booktitle={IJCAI},
  volume={7},
  pages={2802--2807},
  year={2007}
}

@article{claesen2015building,
  title={Building classifiers to predict the start of glucose-lowering pharmacotherapy using belgian health expenditure data},
  author={Claesen, Marc and De Smet, Frank and Gillard, Pieter and Mathieu, Chantal and De Moor, Bart},
  journal={arXiv preprint arXiv:1504.07389},
  year={2015}
}

@article{galarraga2015fast,
  title={Fast rule mining in ontological knowledge bases with AMIE$+$},
  author={Gal{\'a}rraga, Luis and Teflioudi, Christina and Hose, Katja and Suchanek, Fabian M},
  journal={The VLDB Journal},
  volume={24},
  number={6},
  pages={707--730},
  year={2015},
  publisher={Springer-Verlag New York, Inc.}
}

@article{fung2005text,
  title={Text classification without negative examples revisit},
  author={Fung, Gabriel Pui Cheong and Yu, Jeffrey Xu and Lu, Hongjun and Yu, Philip S},
  journal={TKDE},
  volume={18},
  number={1},
  pages={6--20},
  year={2005}
}

@inproceedings{zhou2004learning,
  title={Learning with local and global consistency},
  author={Zhou, Dengyong and Bousquet, Olivier and Lal, Thomas N and Weston, Jason and Sch{\"o}lkopf, Bernhard},
  booktitle={NeurIPS},
  pages={321--328},
  year={2004}
}

@inproceedings{sellamanickam2011pairwise,
  title={A pairwise ranking based approach to learning with positive and unlabeled examples},
  author={Sellamanickam, Sundararajan and Garg, Priyanka and Selvaraj, Sathiya Keerthi},
  booktitle={CIKM},
  pages={663--672},
  year={2011}
}

@article{chawla2002smote,
  title={SMOTE: synthetic minority over-sampling technique},
  author={Chawla, Nitesh V and Bowyer, Kevin W and Hall, Lawrence O and Kegelmeyer, W Philip},
  journal={Journal of artificial intelligence research},
  volume={16},
  pages={321--357},
  year={2002}
}

@inproceedings{deng2017disguise,
  title={Disguise Adversarial Networks for Click-through Rate Prediction.},
  author={Deng, Yue and Shen, Yilin and Jin, Hongxia and others},
  booktitle={IJCAI},
  pages={1589--1595},
  year={2017}
}

@article{Radford99,
author = {M. N. Radford},
year = {1999},
month = {01},
pages = {},
title = {Regression and Classification Using Gaussian Process Priors},
volume = {6},
journal = {Bayesian Statistics}
}

@article{wang05,
   author    = "Y. Wang and M. Papageorgiou",
   title     = "Real-time freeway traffic state estimation based on extended {Kalman} filter: a general approach",
   journal   = "Transport. Res. B-Meth.",
   volume    = 39,
   number   = "2",
   pages     = "141--167",
   year      = 2005 }

@article{Bayen10a,
   author    = "D. B. Work and S. Blandin and {O.-P.} Tossavainen and B. Piccoli and A. Bayen",
   title     = "A Traffic Model for Velocity Data Assimilation",
   journal   = "AMRX",
   volume    = 2010,
   number    = "1",
   pages     = "1--35",
   year      = 2010 }

@article{min11,
   author    = "W. Min and L. Wynter",
   title     = "Real-time road traffic prediction with spatio-temporal correlations",
   journal   = "Transport. Res. C-Emer.",
   volume    = 19,
   number    = "4",
   pages     = "606--616",
   year      = 2011 }

@incollection{MiguelNIPS09,
	title = {Inter-domain Gaussian Processes for Sparse Inference using Inducing Features},
	author = {Miguel L\'{a}zaro-Gredilla and An\'{\i}bal Figueiras-Vidal},
	booktitle = {Advances in Neural Information Processing Systems 22},
	editor = {Y. Bengio and D. Schuurmans and J. D. Lafferty and C. K. I. Williams and A. Culotta},
	pages = {1087--1095},
	year = {2009},
	publisher = {Curran Associates, Inc.},
	url = {http://papers.nips.cc/paper/3876-inter-domain-gaussian-processes-for-sparse-inference-using-inducing-features.pdf}
}

@article{Arik15,
	author = "J. Chen and K. H. Low and P. Jaillet and Y. Yao",
	title = "{Gaussian} Process Decentralized Data Fusion and Active Sensing for Spatiotemporal Traffic Modeling and Prediction in Mobility-on-Demand Systems",
	journal="IEEE Transactions on Automation Science and Engineering",
	volume="12",
	number="3",
	year=2015,
	pages="901--921"
}

@inproceedings{Boots16,
	author = "C.-A. Cheng and B. Boots",
	title = "Incremental Variational Sparse {Gaussian} Process Regression",
	booktitle = "Proc. {NIPS}",
	year = 2016
}

@inproceedings{Bui17,
	author = "T. D. Bui and C. V. Nguyen and R. E. Turner",
	title = "Streaming Sparse {Gaussian} Process Approximations",
	booktitle = "Proc. {NIPS}",
	year = 2017
}

@article{Kang16,
author = {Kang, James Jin and Larkin, Henry},
year = {2016},
month = {01},
pages = {1},
title = {Inference of Personal Sensors in Internet of Things},
volume = {2},
journal = {International Journal of Information, Communication Technology and Applications}
}

@inproceedings{Sarkar14,
	author = "C. Sarkar and S. N. A. U. Nambi and R. V. Prasad and A. Rahim",
	title = "A scalable distributed architecture towards unifying {IoT} applications",
	booktitle = "Proc. IEEE WF-IoT",
	year = 2014
}

@inproceedings{Rakshit17,
author = {Allamraju, Rakshit and Chowdhary, Girish},
year = {2017},
month = {05},
pages = {4442-4447},
title = {Communication efficient decentralized Gaussian Process Fusion for multi-UAS path planning},
booktitle={American Control Conference},
doi = {10.23919/ACC.2017.7963639}
}

@inproceedings{Krahenbuhl11,
	author = "P. Krahenbuhl and V. Koltun",
	title = "Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials",
	booktitle = "Proc. {NIPS}",
	year = 2011
}

@inproceedings{Roger11,
	title = "Bounded approximate decentralised coordination via the max-sum algorithm",
	author = "A. Rogers and  A. Farinelli and R. Stranders and N. R. Jennings",
	booktitle = "Proc. {AIJ}",
	pages = "730--759",
	year = {2011}
}

@techreport{cifar10,
title = "Learning Multiple Layers of Features from Tiny Images",
author = "A. Krizhevsky",
year = {2009}
}

@misc{UCI_protein_data,
author={P. S. Rana},
title={Physicochemical Properties of Protein Tertiary Structure Data Set},
howpublished={http://archive.ics.uci.edu/ml/datasets/},
year={2013}
}

@misc{UCI_diabetes_data,
author={B. Efron and T. Hastie and I. Johnstone and R. Tibshirani},
title="Least Angle Regression, {A}nnals of {S}tatistics, 407-499",
howpublished={https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html},
year={2004}
}


@incollection{LeCun95,
  author = "Y. LeCun and Y. Bengio",
  booktitle = {The Handbook of Brain Theory and Neural Networks},
  editor = {Arbib, Michael A.},
  pages = {255--258},
  publisher = {The MIT Press},
  title = {Convolutional Networks for Images, Speech, and Time Series},
  year = 1995
}

@inproceedings{Liu18,
title = "Generalized Robust {Bayesian} Committee Machine for Large-scale {Gaussian} Process Regression",
author = "H. Liu and J. Cai and Y. Wang and Y.-S. Ong",
booktitle = "Proc. {ICML}",
year = 2018
}

@inproceedings{Wang17b,
title = "Max-value Entropy Search for Efficient {Bayesian} Optimization",
author = "Z. Wang and S. Jegelka",
booktitle = "Proc. {ICML}",
pages = "3627--3635",
year = 2017
}

@inproceedings{Li16,
author = "C.-L. Li and K. Kandasamy and B. P\'{o}czos and J. Schneider",
title = "High Dimensional {Bayesian} Optimization via Restricted Projection Pursuit Models",
booktitle = "Proc. {AISTATS}",
year = "2016"
}

@inproceedings{Duvenaud11,
title = "Additive {Gaussian} Processes",
author = "D. K. Duvenaud and H. Nickisch and C. E. Rasmussen",
booktitle = "Proc. {NIPS}",
pages = "226--234",
year = 2011
}

@inproceedings{Garnett17,
author = "J. R. Gardner and C. Guo and K. Q. Weinberger and R. Garnett and R. Grosse",
title = "Discovering and Exploiting Additive Structure for Bayesian Optimization",
booktitle = "Proc. {AISTATS}",
year = "2017"
}

@inproceedings{Erik17,
title = "Distributed Batch {Gaussian} Process Optimization",
author = "E. Daxberger and K. H. Low",
booktitle = "Proc. {ICML}",
pages = "951--960",
year = 2017
}

@article{shahriari16,
	author="Shahriari, B. and  Swersky, K. and Wang, Z. and  Adams, R.P.  and {de Freitas}, N.",
	title="Taking the Human Out of the Loop: A Review of {Bayesian} Optimization",
	journal="Proceedings of the IEEE",
	volume="104",
	number="1",
	year=2016,
	pages="148--175"
}

@article{villemonteix09,
  author    = "J. Villemonteix and E. Vazquez and E. Walter",
  title     = "An informational approach to the global optimization of expensive-to-evaluate functions",
  journal   = "J. Glob. Optim.",
  volume    = 44,
  number = "4",
  year      = {2009},
  pages     = "509--534"
}

@article{Chapman11,
   author    = "A. Chapman and A. Rogers and N. R. Jennings and D. Leslie",
   title     = "A Unifying Framework for Iterative Approximate Best-Response Algorithms for Distributed Constraint Optimisation Problems",
   journal   = "The Knowledge Engineering Review",
   volume    = 26,
   number = 4,
   pages     = "411--444",
   year      = 2011 }

@article{Leite14,
   author    = "A. R. Leite and F. Enembreck and J.-P. A. Barth{\`{e}}s",
   title     = "Distributed Constraint Optimization Problems: Review and perspectives",
   journal   = "Expert Systems with Applications",
   volume    = 41,
   number = 11,
   pages     = "5139--5157",
   year      = 2014 }

@inproceedings{Yarin15,
title = "Improving the {G}aussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs",
author = "Y. Gal and R. Turner",
booktitle = "Proc. {ICML}",
pages = "655-664",
year = 2015
}

@inproceedings{Tresp02,
title = "Transductive and inductive method for approximate {G}aussian process regression",
author = "A. Schwaighofer and V. Tresp",
booktitle = "Proc. {NIPS}",
pages = "953--960",
year = 2002
}

@inproceedings{Titsias14,
title = "Doubly Stochastic Variational {B}ayes for non-Conjugate Inference",
author = "M. K. Titsias and M. {L\'{a}zaro}-Gredilla",
booktitle = "Proc. {ICML}",
year = 2014
}

@inproceedings{Titsias13,
title = "Variational Inference for {M}ahalanobis Distance Metrics in {G}aussian Process Regression",
author = "M. K. Titsias and M. {L\'{a}zaro}-Gredilla",
booktitle = "Proc. {NIPS}",
year = 2013,
pages = "279--287"
}

@inproceedings{NghiaICML16,
title = "A Distributed Variational Inference Framework for Unifying Parallel Sparse {G}aussian Process Regression Models",
author = "T. N. Hoang and Q. M. Hoang and K. H. Low",
booktitle = "Proc. {ICML}",
pages = "382--391",
year = 2016
}


@article{Jordan08,
   author    = "M. J. Wainwright and M. I. Jordan",
   title     = "Graphical Models, Exponential Families, and Variational Inference",
   journal   = "Foundations and Trends$^{\textregistered}$ in Machine Learning",
   volume    = 1,
   number    = "1--2",
   pages     = "1--305",
   year      = 2008 }
   
@inproceedings{Marco2008,
 title = "On Entropy Approximation for {Gaussian} Mixture Random Vectors",
 author = "M. Huber and T. Bailey and H. {Durrant-Whyte} and U. D. Hanebeck",
   booktitle = "Proc. {IEEE} International Conference on Multisensor Fusion and Integration for Intelligent Systems",
   pages     = "181--188",
   year      = 2008
 }
 
@inproceedings{How15,
 title = "Streaming, Distributed Variational Inference for {Bayesian} Nonparametrics",
author = "T. Campbell and J. Straub and J. W. {Fisher III} and J. P. How",
booktitle = "Proc. NIPS",
pages = {280--288},
year = {2015}

}

@inproceedings{Gordon05,
   author    = "H. B. McMahan and M. Likhachev and G. J. Gordon",
   title     = "Bounded Real-Time Dynamic Programming: {RTDP} with Monotone Upper Bounds and Performance Guarantees",
   booktitle = "Proc. {ICML}",
   pages     = "569--576",
   year      = 2005 }

@inproceedings{Simmons06,
   author    = "T. Smith and R. Simmons",
   title     = "Focused Real-Time Dynamic Programming for {MDP}s: Squeezing More Out of a Heuristic",
   booktitle = "Proc. {AAAI}",
   pages = "1227--1232",
   year      = 2006 }
   
@book{Rue05,
   author    = "H. Rue and L. Held",
   title     = "{Gaussian Markov} Random Fields: Theory and Applications",
   publisher = "Chapman \& Hall/CRC",
   year      = 2005 }

@book{Soong04,
   author    = "T. T. Soong",
   title     = "Fundamentals of Probability and Statistics for Engineers",
   publisher = "John Wiley \& Sons",
   year      = 2004 }

@article{Shewry87,
   author    = "M. C. Shewry and H. P. Wynn",
   title     = "Maximum Entropy Sampling",
   journal   = "J. Applied Statistics",
   volume    = 14,
   number    = 2,
   pages     = "165--170",
   year      = 1987 }
   
@book{Cover91,
   author    = "T. Cover and J. Thomas",
   title     = "Elements of Information Theory",
   publisher = "John Wiley \& Sons",
   address   = "NY",
   year      = 1991 }

@inproceedings{Isler13,
  title = 	 "Sensor Planning for a Symbiotic {UAV} and {UGV} system for Precision Agriculture",
  author = 	 "P. Tokekar and J. V. Hook and D. Mulla and V. Isler",
  booktitle = "Proc. {IROS}",
  year = 	 2013
 }

@inproceedings{Natarajan_2012,
   author = "Natarajan, P. and Hoang, T. N. and Low, K. H. and Kankanhalli, M.",
    booktitle = "Proc. {AAMAS}",
    title = "Decision-Theoretic Approach to Maximizing Observation of Multiple Targets in Multi-Camera Surveillance",
    year = 2012,
    pages = "155--162"
}

@inproceedings{Natarajan_2012_ext,
   author = "Natarajan, P. and Hoang, T. N. and Low, K. H. and Kankanhalli, M.",
   booktitle    = "Proc. {ICDSC}",
    title = "Decision-Theoretic Coordination and Control for Active Multi-Camera Surveillance in Uncertain, Partially Observable Environments",
    year = 2012
   }

@techreport{Anonymous2012,
 title = "A General Framework for Interacting {Bayes}-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior",
 author = "T. N. Hoang and K. H. Low",
   type     = "{arXiv:1304.2024}",
 year = {2013}
 }

@techreport{Nghia19,
	title = "Collective Online Learning of {G}aussian Processes in Massive Multi-Agent Systems",
	author = "T. N. Hoang and Q. M. Hoang and K. H. Low and J. P. How",
	type     = "{arXiv:1805.09266}",
	year = {2018}
}

@inproceedings{NghiaAAAI19,
	title = "Collective Online Learning of {G}aussian Processes in Massive Multi-Agent Systems",
	author = "T. N. Hoang and Q. M. Hoang and K. H. Low and J. P. How",
	booktitle = "Proc. {AAAI}",
	year = {2019}
}

@inproceedings{NghiaICML19,
	title = "Collective Model Fusion of Multiple Black-Box Experts",
	author = "Q. M. Hoang and T. N. Hoang and K. H. Low and C. Kingsford",
	booktitle = "Proc. {ICML}",
	year = {2019}
}

@inproceedings{NghiaNIPS19,
	title = "Statistical Model Aggregation via Parameter Matching",
	author = "M. Yurochkin and M. Argawal and S. Ghosh and K. Greenewald and T. N. Hoang",
	booktitle = "Proc. {NeurIPS}",
	year = {2019},
	pages = "10954--10964"
}


@inproceedings{LowAAMAS13,
 title = "Multi-Robot Informative Path Planning for Active Sensing of Environmental Phenomena: A Tale of Two Algorithms",
 author = "N. Cao and K. H. Low and J. M. Dolan",
   booktitle = "Proc. {AAMAS}",
   pages = "7--14",
 year = {2013}
 }

@inproceedings{LowUAI12,
 title = "Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena",
 author = "J. Chen and K. H. Low and C. K.-Y. Tan and A. Oran and P. Jaillet and J. M. Dolan and G. S. Sukhatme",
   booktitle = "Proc. UAI",
   pages     = "163--173",
   year      = 2012
 }

@inproceedings{LowAAMAS12,
   author    = "K. H. Low and J. Chen and J. M. Dolan and S. Chien and D. R. Thompson",
   title     = "Decentralized Active Robotic Exploration and Mapping for Probabilistic Field Classification in Environmental Sensing",
   booktitle = "Proc. {AAMAS}",
   pages     = "105--112",   
   year      = 2012 }

@inproceedings{LowAAMAS11,
   author    = "K. H. Low and J. M. Dolan and P. Khosla",
   title     = "Active {Markov} Information-Theoretic Path Planning for Robotic Environmental Sensing",
   booktitle = "Proc. {AAMAS}",
   pages     = "753--760",
   year      = 2011 }
   
@inproceedings{LowICAPS09,
   author    = "K. H. Low and J. M. Dolan and P.  Khosla",
   title     = "Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing",
   booktitle = "Proc. {ICAPS}",
   pages = "233--240",
   year      = 2009 }
   
@inproceedings{LowAAMAS08,
   author    = "K. H. Low and J. M. Dolan and P. Khosla",
   title     = "Adaptive Multi-Robot Wide-Area Exploration and Mapping",
   booktitle = "Proc. {AAMAS}",
   pages     = "23--30",
   year      = 2008 }   

@inproceedings{LowICRA07,
   author    = "K. H. Low and G. J. Gordon and J. M. Dolan and P. Khosla",
   title     = "Adaptive Sampling for Multi-Robot Wide-Area Exploration",
   booktitle = "Proc. {IEEE ICRA}",
   pages = "755--760",
   year      = 2007 }
      
@inproceedings{NghiaIJCAI13a,
  title = 	 "Interactive {POMDP Lite}: Towards Practical Planning to Predict and Exploit Intentions for Interacting with Self-Interested Agents",
  author = 	 "T. N. Hoang and K. H. Low",
  booktitle = "Proc. {IJCAI}",
  year = 	 2013
 }

@inproceedings{NghiaIJCAI13b,
  title = 	 "A General Framework for Interacting {Bayes}-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior",
  author = 	 "T. N. Hoang and K. H. Low",
  booktitle = "Proc. {IJCAI}",
  pages = "1394--1400",
  year = 	 2013
 }
 
 @inproceedings{NghiaAAMAS12,
   author    = "T. N. Hoang and K. H. Low",
   title     = "Intention-Aware Planning under Uncertainty for Interacting with Self-Interested, Boundedly Rational Agents",
   booktitle = "Proc. {AAMAS}",
   pages     = "1233--1234",   
   year      = 2012 }
 
  @inproceedings{LowSPIE09,
   author    = "J. M. Dolan and G. Podnar and S. Stancliff and K. H. Low and A. Elfes and J. Higinbotham and J. C. Hosler and T. A. Moisan and J. Moisan",
   title     = "Cooperative Aquatic Sensing using the Telesupervised Adaptive Ocean Sensor Fleet",
   booktitle = "Proc. {SPIE} Conference on Remote Sensing of the Ocean, Sea Ice, and Large Water Regions",
   volume = 7473,
   year      = 2009 } 

@inproceedings{LowUAI13,
 title = "Parallel {Gaussian} Process Regression with Low-Rank Covariance Matrix Approximations",
 author = "J. Chen and N. Cao and K. H. Low and R. Ouyang and C. K.-Y. Tan and P. Jaillet",
   booktitle = "Proc. UAI",
   pages     = "152--161",
   year      = 2013
 }
    
 @inproceedings{LowAeroconf10,
   author    = "G. Podnar and J. M. Dolan and K. H. Low and A. Elfes",
   title     = "Telesupervised Remote Surface Water Quality Sensing",
   booktitle = "Proc. {IEEE} Aerospace Conference",
   year      = 2010 }
 
 @inproceedings{LowAAMAS14,
   author    = "R. Ouyang and K. H. Low and J. Chen and P. Jaillet",
   title     = "Multi-Robot Active Sensing of Non-Stationary {Gaussian} Process-Based Environmental Phenomena",
   booktitle = "Proc. {AAMAS}",   
   year      = 2014 }
 
 @article{LowRSS13,
author = {Chen, Jie and Low, Kian Hsiang and Tan, Colin},
year = {2013},
month = {06},
pages = {},
title = {Gaussian Process-Based Decentralized Data Fusion and Active Sensing for Mobility-on-Demand System},
journal={Robotics: Science and System}
}
   
 @inproceedings{Anonymous2012a,
 title = {Planning to Predict Intentions for Interacting with Self-Interested, Boundedly Rational Agents},
 author = {Anonymous},
 booktitle = {http://dl.dropbox.com/u/19431783/extended.pdf},
 year = {2012}
 }

@phdthesis{Murphy2002,
  author = 	 {Kevin Murphy},
  title = 	 {Dynamic Bayesian Networks: Representation, Inference and Learning},
  school = 	 {University of California, Berkeley},
  year = 	 {2002},
  address = 	 {387 Soda Hall, Berkeley CA 94720},
  month = 	 {July}
}

@phdthesis{Gifford09,
  author = 	 {C. M. Gifford},
  title = 	 {Collective Machine Learning: Team learning and classification in multi-agent systems},
  school = 	 {University of Kansas Lawrence},
  year = 	 {2009},
  address = 	 {Kansas, USA}
}


@manual{TIAB2003,
  title = 	 {Thesis in a Box},
  author = 	 {Mark A. Paskin},
  organization = {University of California, Berkeley},
  address = 	 {387 Soda Hall, Berkeley CA 94720},
  month = 	 {August},
  year = 	 {2003}
}

@inproceedings{Hansen2004,
  title = 	 {Dynamic programming for partially observable stochastic games},
  author = 	 {E. A. Hansen and D. S. Bernstein and S. Zilberstein},
  booktitle = "Proc. {AAAI}",
  pages = "709--715",
  year = 	 {2004}
 }
 
@inproceedings{Oliehoek2007,
  title = 	 {Q-value functions for decentralized {POMDP}s},
  author = 	 {F. A. Oliehoek and N. Vlassis},
  booktitle = {Proc. of Int. Joint Conference on Autonomous Agents and Multi Agent Systems},
  month = {May},
  year = 	 {2007}
 }
 
@article{Oliehoek2008b,
  title = 	 {Optimal and approximate Q-value functions for decentralized {POMDP}s},
  author = 	 {F. A. Oliehoek and N. Vlassis},
  journal = {Journal of Artificial Intelligence Research},
  year = 	 {2008}
 } 
 
 @inproceedings{Oliehoek2009,
  title = 	 {Lossless clustering of histories in decentralized {POMDP}s},
  author = 	 {F. A. Oliehoek and S. Whiteson and M. T. J. Spaan},
  booktitle = {Proc. of Int. Joint Conference on Autonomous Agents and Multi Agent Systems},
  month = {May},
  year = 	 {2009}
 }
 
 @inproceedings{Oliehoek2010,
  title = 	 {Heuristic search for identical payoff Bayesian games},
  author = 	 {F. A. Oliehoek and M. T. J. Spaan and J. S. Dibangoye and C. Amato},
  booktitle = {Proc. of Int. Joint Conference on Autonomous Agents and Multi Agent Systems},
  month = {May},
  year = 	 {2010}
 }
 
 @inproceedings{Pineau2003,
  title = 	 {Point-based value iteration: An anytime algorithm for {POMDP}s},
  author = 	 {J. Pineau and G. Gordon and S. Thrun},
  booktitle = {Proc. IJCAI},
  pages = "1025--1032",
  year = 	 {2003}
 }
 
 @article{Petrik2009,
  title = 	 {A bilinear programming approach for multiagent planning},
  author = 	 {M. Petrik and S. Zilberstein},
  journal = {Journal of Artificial Intelligence Research},
  year = 	 {2009}
 } 
 
 @book{Russel2009,
  title = 	 {Artificial Intelligence : A Modern Approach, 3rd Edition},
  author = 	 {S. J. Russel and P. Norvig},
  publisher = {Prentice Hall},
  year = 	 {2009}
 } 
 
 @phdthesis{Sondik1971,
  author = 	 {E. J. Sondik},
  title = 	 {The optimal control of partially observable Markov processes},
  school = 	 {Stanford University},
  year = 	 {1971}
 }
  
 @inproceedings{Seuken2007b,
  title = 	 {Memory-bounded dynamic programming for {DEC-POMDP}s},
  author = 	 {S. Seuken and S. Zilberstein},
  booktitle = {Proc. IJCAI},
  pages = "2009--2015",
  year = 	 {2007}
 } 

@inproceedings{Spaan2008,
  title = 	 {Multiagent planning under uncertainty with stochastic communication delays},
  author = 	 {M. T. J. Spaan and F. A. Oliehoek and N. Vlassis},
  booktitle = {Proc. ICAPS},
  year = 	 {2008}
 }
 
 @inproceedings{Spaan2011,
  title = 	 "Scaling up optimal heuristic search in {DEC-POMDP}s via incremental expansion",
  author = 	 "M. T. J. Spaan and F. A. Oliehoek and C. Amato",
  booktitle = "Proc. IJCAI",
  pages = "2027--2032",
  year = 	 {2011}
 }
 
 @inproceedings{Szer2005,
  title = 	 {MAA*: A heuristic search algorithm for solving decentralized {POMDP}s },
  author = 	 {D. Szer and F. Charpillet and S. Zilberstein},
  booktitle = {Proc. of Uncertainty in Artificial Intelligence},
  year = 	 {2005}
 }
 
 @inproceedings{Boularias2008,
  title = 	 {Exact dynamic programming for decentralized {POMDP}s with lossless policy compression},
  author = 	 {A. Boularias and B. Chaib-draa},
  booktitle = {Proc. of Int. Conference on Automated Planning and Scheduling},
  year = 	 {2008}
 }
 
 @inproceedings{Aras2007,
  title = 	 {Mixed integer linear programming for exact finite-horizon planning in decentralized {POMDP}s},
  author = 	 {R. Aras and A. Dutech and F. Charpillet},
  booktitle = {Proc. of Int. Conference on Automated Planning and Scheduling},
  year = 	 {2003}
 }

@inproceedings{Emery2004,
  title = 	 {Approximate solutions for partially observable stochastic games with common payoffs},
  author = 	 "R. {Emery-Montemerlo} and G. J. Gordon and J. G. Schneider and  S. Thrun",
  booktitle = {Proc. AAMAS},
  pages = "136--143",
  year = 	 {2004}
 }
 
@article{Doshi2005,
  title = 	 {A framework for sequential planning in multi-agent settings},
  author = 	 {P. J. Gmytrasiewicz and P. Doshi},
  journal = {JAIR},
  volume = 24,
  pages = "49--79",
  year = 	 {2005}
 }
 
@inproceedings{Seuken2007a,
  title = 	 {Improved memory-bounded dynamic programming for decentralized {POMDP}s},
  author = 	 {S. Seuken and S. Zilberstein},
  booktitle = {Proc. of Uncertainty in Artificial Intelligence},
  month = {July},
  year = 	 {2007}
 }
 
@article{Spaan2005,
  title = 	 {Perseus: Randomized point-based value iteration for {POMDP}s},
  author = 	 {M. T. J. Spaan and N. Vlassis},
  journal = {Journal of Artificial Intelligence Research},
  year = 	 {2005}
 }

@inproceedings{Nair2003,
  title = 	 {Taming decentralized {POMDP}s: Towards efficient policy computation for multiagent settings},
  author = 	 {R. Nair and M. Tambe},
  booktitle = {Proc. IJCAI},
  pages = "705--711",
  year = 	 {2003}
 }

@inproceedings{Bernstein2000,
  title = 	 {The Complexity of Decentralized Control of Markov Decision Processes},
  author = 	 {Daniel S. Bernstein and Shlomo Zilberstein and Neil Immerman},
  booktitle = {Proc. of the 16th Conference on Uncertainty in Artificial Intelligence},
  month = {June},
  year = 	 {2000}
 }

@inproceedings{Piotr2005,
  title = 	 {A Particle Filtering based Approach to Approximate Interactive {POMDP}s},
  author = 	 {P. Doshi and P. Gmytrasiewicz},
  booktitle = {Proc. AAAI},
  pages = "969--974",
  year = 	 {2005}
 }
 
@article{Doshi2009,
  title = 	 {{Monte Carlo} Sampling Methods for Approximating Interactive {POMDP}s},
  author = 	 {P. Doshi and P. Gmytrasiewicz},
  journal = {JAIR},
  pages = "297--337",
  year = 	 {2009}
 }
 
@inproceedings{Doshi2008,
  title = 	 {Generalized Point Based Value Iteration for Interactive {POMDP}s},
  author = 	 {P. Doshi and D. Perez},
  booktitle = {Proc. AAAI},
  pages = "63--68",
  year = 	 {2008}
 }

@article{Hauskretcht2000,
  title = 	 {Value function approximations for partially observable Markov decision processes},
  author = 	 {M. Hauskretcht},
  journal = {Journal of Artificial Intelligence Research},
  year = 	 {2000}
 }
 
@article{Kaelbling1998,
  title = 	 {Planning and acting in partially observable stochastic domains},
  author = 	 {L. P. Kaelbling and M. L. Littman and A. R. Cassandra},
  journal = {Artificial Intellige},
  year = 	 {1998}
 }
 
@inproceedings{Littman1995,
  title = 	 {Learning policies for partially observable environments : Scaling up},
  author = 	 {M. L. Littman and A. R. Cassandra and L. P. Kaelbling},
  booktitle = {Proc. of the Int. Conference on Machine Learning},
  year = 	 {1995}
 }

@inproceedings{Smith2004,
  title = 	 {Heuristic search value iteration for {POMDP}s},
  author = 	 {T. Smith and R. Simmons},
  booktitle = {Proc. of Uncertainty in Artificial Intelligence},
  year = 	 {2004}
 }
 
@inproceedings{Smith2005,
  title = 	 {Point-based POMDP algorithms : Improved analysis and implementation},
  author = 	 {T. Smith and R. Simmons},
  booktitle = {Proc. of Uncertainty in Artificial Intelligence},
  year = 	 {2005}
 } 
 
 @inproceedings{Hsu2007,
  title = 	 {What makes some POMDP problems easy to approximate?},
  author = 	 {D. Hsu and W. Lee and N. Rong},
  booktitle = {Proc. of Advances in Neural Information Processing Systems},
  year = 	 {2007}
 }
 
 @inproceedings{Kurniawati2008,
  title = 	 {SARSOP: Efficient point-based POMDP planning by approximating optimally reachable belief spaces},
  author = 	 {H. Kurniawati and D. Hsu and W. S. Lee},
  booktitle = {Proc. Robotics: Science and Systems},
  year = 	 {2008}
 }
 
 @inproceedings{Michael2011,
  title = 	 {Using Iterated Reasoning to Predict Opponent Strategies},
  author = 	 {Michael Wunder and Michael Kaisers and John Robert Yaros and Michael Littman},
  booktitle = {Proc. of 10th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2011)},
  month = {May},
  year = 	 {2011}
 }

 @inproceedings{David2010,
  title = 	 {Monte-Carlo planning in large {POMDP}s},
  author = 	 {David Silver and Joel Veness},
  booktitle = {Proc. of Advances in Neural Information Processing Systems},
  year = 	 {2010}
 }

@article{Hu2003,
  title = 	 {Nash Q-Learning for General-Sum Stochastic Games},
  author = 	 {J. Hu and Michael P. Wellman},
  journal = {JMLR},
  month = {November},
  year = 	 {2003}
 }

 @article {Shoham2007,
        author = { Y. Shoham and R. Powers and T. Grenager},
        journal = {AIJ},
        title = {If Multi-Agent Learning is the Answer, What is the Question? },
        volume = "171",
        number= 7,
	pages = {365--377},
	Year = 2007
        }

@inproceedings{Amato2009,
  title = 	 {Incremental policy generation for finite-horizon DEC-{POMDP}s},
  author = 	 {C. Amato and J. Dibangoye and S. Zilberstein},
  booktitle = {Proc. of Int. Conference on Automated Planning and Scheduling},
  year = 	 {2009}
 }

@inproceedings{Amato2009a,
  title = 	 {Achieving Goals in Decentralized {POMDP}s},
  author = 	 {C. Amato and S. Zilberstein},
  booktitle = {Proc. of Int. Joint Conference on Autonomous Agents and Multi Agent Systems},
  year = 	 {2009}
 }

@inproceedings{Carlin2008,
  title = 	 {Value-based observation compression for DEC-{POMDP}s},
  author = 	 {A. Carlin and S. Zilberstein},
  booktitle = {Proc. of Int. Joint Conference on Autonomous Agents and Multi Agent Systems},
  year = 	 {2008}
 }

@inproceedings{Dibangoye2009,
  title = 	 {Point-based Incremental Pruning Heuristic for Solving Finite Horizon DEC-{POMDP}s},
  author = 	 {J. S. Dibangoye and A. I. Mouaddib and B. Chaib-draa},
  booktitle = {Proc. of Int. Joint Conference on Autonomous Agents and Multi Agent Systems},
  year = 	 {2009}
 }

@article{Oliehoek2008a,
  title = 	 {The cross-entropy method for policy search in decentralized {POMDP}s},
  author = 	 {F. A. Oliehoek and Kooi J. F. and Vlassis N.},
  journal = {Informatica},
  year = 	 {2008}
 }

@article{Eker2008,
  title = 	 {Using evolution strategies to solve DEC-POMDP problems},
  author = 	 {B. Eker and H. L. Akin},
  journal = {Soft Computing - A Fusion of Foundations, Methodologies and Applications},
  year = 	 {2008}
 }

@book{Fudenberg1991,
  title = 	 {Game Theory},
  author = 	 {D. Fudenberg and J. Tirole},
  publisher = {MIT Press},
  year = 	 {1991}
 } 
 
 @inproceedings{Littman1994,
  title = 	 {Markov games as a framework for multi-agent reinforcement learning},
  author = 	 {M. L. Littman},
  booktitle = {Proc. ICML},
  pages = "157--163",
  year = 	 {1994}
 }
 
 @inproceedings{Hu1998,
  title = 	 {Multi-agent reinforcement learning: Theoretical framework and an algorithm},
  author = 	 {J. Hu and M. P. Wellman},
  booktitle = {Proc. ICML},
  pages = "242--250",
  year = 	 {1998}
 }
 
 @inproceedings{Koller2001,
  title = 	 {Multi-agent influence diagrams for representing and solving games},
  author = 	 {D. Koller and B. Milch},
  booktitle = {Proc. of 17th Int. Joint Conference on Artificial Intelligence},
  year = 	 {20013}
 }

 @article{Nash1951,
  title = 	 {Non-cooperative games},
  author = 	 {John F. Nash},
  journal = {Ann. Math.},
  year = 	 {1951}
 }

@book{Sutton1998,
  title = 	 {Reinforcement Learning: An Introduction},
  author = 	 {Richard S. Sutton and Andrew G. Barto},
  publisher = {MIT Press/Bradford Books},
  month = {March},
  year = 	 {1998}
 } 
 
@book{Bazaraa1979,
  title = 	 {Nonlinear programming: Theory and algorithms},
  author = 	 {Mokhtar S. Bazaraa and C. M. Shetty},
  publisher = {John Wiley and Sons},
  year = 	 {1979}
 } 

@book{Binmore1990,
  title = 	 {Essays on Foundations of Game Theory},
  author = 	 {K. Binmore},
  publisher = {Blackwell},
  year = 	 {1990}
 } 

 @article{Larkey1982,
  title = 	 {Subjective probability and the theory of games},
  author = 	 {J. B. Kadane and P. D. Larkey},
  journal = {Management Science},
  year = 	 {1982}
 }
 
 @inproceedings{Halpern2008,
  title = 	 {Beyond Nash equilibrium: Solution concepts for the 21st century},
  author = 	 {Joseph Y. Halpern},
  booktitle = {Proc. of 27th Annual ACM Symposium on Principles of Distributed Computing},
  year = 	 {2008}
 }

@article{Camerer2004,
  title = 	 {A cognitive hierarchy model of games},
  author = 	 {C. F. Camerer and T. H. Ho and J. K. Chong},
  journal = {Quarterly J. Economics},
  volume = 119,
  number = 3,
  pages = "861--898",
  year = 	 {2004}
 }

@book{Reinhard2002,
  title = 	 {Bounded Rationality},
  author = 	 {Gerd Gigerenzer and Reinhard Selten},
  publisher = {MIT Press},
  year = 	 {2002}
 } 
 
 @inproceedings{Doshi2010,
  title = 	 {Modeling Recursive Reasoning by Humans using Empirically Informed Interactive {POMDP}s},
  author = 	 {P. Doshi and Xia Qu and Adam Goodie and Diana Young},
  booktitle = {Proc. AAMAS},
  pages = "1223--1230",
  year = 	 {2010}
 }

@inproceedings{Brenda2010,
  title = 	 {Towards Applying Interactive {POMDP}s to Real-World Adversary Modeling},
  author = 	 {Brenda Ng and Carol Meyers and Kofi Boakye and John Nitao},
  booktitle = {Proc. IAAI},
  pages = "1814--1820",
  year = 	 {2010}
 }

@inproceedings{Doshi2006,
  title = 	 {Exact Solutions of Interactive {POMDP}s Using Behavioral Equivalence},
  author = 	 {Bharanee Rathnasabapathy and P. Doshi and Piotr Gmytrasiewicz},
  booktitle = {Proc. AAMAS},
  pages = "1025--1032",
  year = 	 {2006}
 }
 
 @inproceedings{Oliehoek05benelearn,
  author =       {Frans Oliehoek and Matthijs T. J. Spaan and Nikos
                  Vlassis},
  title =        {Best-response play in partially observable card
                  games},
  booktitle =    {Benelearn 2005: Proceedings of the 14th Annual
                  Machine Learning Conference of Belgium and the
                  Netherlands},
  month =        feb,
  year =         2005,
  pages =        {45--50}
}

@inproceedings{Littman2001,
  title = 	 "Friend-or-Foe {Q}-learning in general-sum games",
  author = 	 {Michael L. Littman},
  booktitle = {Proc. ICML},
  year = 	 {2001}
 }
 
@inproceedings{Tuyls2003,
  title = 	 {A Selection - Mutation Model for Q-learning in Multi-agent Systems},
  author = 	 {Karl Tuyls and Katja Verbeeck and Tom Lenaerts},
  booktitle = {Proc. AAMAS},
  year = 	 {2003},
  pages = {693--700}
 }
  
 @techreport{Greenwald2005,
 title = {Correlated Q-Learning},
 author = {A. Greenwald and K. Hall and M. Zinkevich},
 institution = {Brown University},
 address = 	 {Providence, Rhode Island 02912},
 month = 	 {July},
 year = 	 {2005}
 }

@inproceedings{Bianchi2007,
  title = 	 {Heuristic Selection of Actions in Multiagent Reinforcement Learning},
  author = 	 {R. A. C. Bianchi and C. H. C. Ribeiro and A. H. R. Costa},
  booktitle = {Proc. IJCAI},
  year = 	 {2007}
 }

@inproceedings{Akchurina2009,
  title = 	 "Multiagent Reinforcement Learning: Algorithm Converging to {Nash} Equilibrium in General-Sum Discounted Stochastic Games",
  author = 	 {N. Akchurina},
  booktitle = {Proc. AAMAS},
  year = 	 {2009},
  pages = {725--732}
 }

@inproceedings{Vrancx2008,
  title = 	 {Switching Dynamics of Multi-Agent Learning},
  author = 	 {P. Vrancx and K. Tuyls and R. Westra},
  booktitle = {Proc. AAMAS},
  year = 	 {2008}
 }

@inproceedings{Hennes2010,
  title = 	 "{RESQ}-learning in stochastic games",
  author = 	 {D. Hennes and M. Kaisers and K. Tuyls},
  booktitle = "Proc. {AAMAS} Workshop on Adaptive and Learning Agents",
  year = 	 {2010}
 }
 
 @inproceedings{Singh2000,
  title = 	 {Nash Convergence of Gradient Dynamics in General-Sum Games},
  author = 	 {S. Singh and M. Kearns and Y. Mansour},
  booktitle = {Proc. ICML},
  year = 	 {2000}
 }
 
 @inproceedings{Bowling2001,
  title = 	 {Rational and Convergent Learning in Stochastic Games},
  author = 	 {M. Bowling and M. Veloso},
  booktitle = {Proc. IJCAI},
  year = 	 {2001}
 }
 
 @inproceedings{Suematsu2002,
  title = 	 {A Multiagent Reinforcement Learning Algorithm using Extended Optimal Response},
  author = 	 {N. Suematsu and A. Hayashi},
  booktitle = {Proc. AAMAS},
  year = 	 {2002}
 }

@inproceedings{Conitzer2003,
  title = 	 {AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents},
  author = 	 {V. Conitzer and T. Sandholm},
  booktitle = {Proc. ICML},
  year = 	 {2003}
 }

@inproceedings{Powers2005,
  title = 	 {Learning against opponents with bounded memory},
  author = 	 {R. Powers and Y. Shoham},
  booktitle = {Proc. IJCAI},
  year = 	 {2005}
 }

@inproceedings{Chalkiadakis2003,
  title = 	 "Coordination in Multiagent Reinforcement Learning: A {Bayesian} Approach",
  author = 	 {G. Chalkiadakis and C. Boutilier},
  booktitle = {Proc. AAMAS},
  pages = "709--716",
  year = 	 {2003}
 }
 
 @phdthesis{Chalkiadakis2007,
  author = 	 {G. Chalkiadakis},
  title = 	 {A BAYESIAN APPROACH TO MULTIAGENT REINFORCEMENT LEARNING AND COALITION FORMATION UNDER UNCERTAINTY},
  school = 	 {University of Toronto, Canada},
  year = 	 {2007}
}

@inproceedings{Poupart2006,
  title = 	 "An Analytic Solution to Discrete {Bayesian} Reinforcement Learning",
  author = 	 {P. Poupart and N. Vlassis and J. Hoey and K. Regan},
  booktitle = {Proc. ICML},
  pages = "697--704",
  year = 	 {2006}
 }

@inproceedings{Claus1998,
  title = 	 {The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems},
  author = 	 {C. Claus and C. Boutilier},
  booktitle = {Proc. AAAI},
  year = 	 {1998},
  pages = {746--752}
 }

@inproceedings{Dearden1998,
  title = 	 "Bayesian {Q}-learning",
  author = 	 {R. Dearden and N. Friedman and S. Russell},
  booktitle = {Proc. AAAI},
  pages = "761--768",
  year = 	 {1998}
 }

@inproceedings{LowDyDESS14,
  title = "Recent advances in scaling up {G}aussian process predictive models for large spatiotemporal data",
  author = {K. H. Low and J. Chen and T. N. Hoang and N. Xu and P. Jaillet},
  booktitle = {Proc. DyDESS},
  pages = "167--181",
  year = {2015}
}

@inproceedings{Tesauro2003,
  title = 	 "Extending {Q}-learning to General Adaptive Multi-Agent Systems",
  author = 	 {G. Tesauro},
  booktitle = {Proc. NIPS},
  year = 	 {2003}
 }
 
 @article{Gipps1981,
  title = 	 {A behavioural car following model for computer simulation},
  author = 	 {P. G. Gipps},
  journal = {Transportation Research B},
  volume = 15,
  number = 2,
  pages = "105--111",
  year = 	 {1981}
 }
 
 @inproceedings{Ziebart2010,
  title = 	 "Maximum Entropy Reinforcement Learning",
  author = 	 {B. D. Ziebart and A. Maas and J. A. Bagnell and A. K. Dey},
  booktitle = {Proc. AAAI},
  year = 	 {2010}
 }

@inproceedings{Prabhu2012a,
  title = 	 "Decision-Theoretic Approach to Maximizing Observation of Multiple Targets in Multi-Camera Surveillance",
  author = 	 {P. Natarajan and T. N. Hoang and K. H. Low and M. Kankanhalli},
  booktitle = {Proc. AAMAS},
  pages = "155--162",
  year = 	 {2012}
 }

@inproceedings{Prabhu2012b,
  title = 	 "Decision-Theoretic Coordination and Control for Active Multi-Camera Surveillance in Uncertain, Partially Observable Environments",
  author = 	 {P. Natarajan and T. N. Hoang and K. H. Low and M. Kankanhalli},
  booktitle = {Proc. ICDSC},
  year = 	 {2012}
 }
 
 @inproceedings{Prabhu2014,
 title = "Scalable Decision-Theoretic Coordination and Control for Real-time Active Multi-Camera Surveillance",
 author = {P. Natarajan and T. N. Hoang and Y. Wong and K. H. Low and M. Kankanhalli},
 booktitle = {Proc. ICDSC},
 year = {2014}
 }
 
@inproceedings{Duff2003,
title = "Design for an optimal probe",
author = {M. Duff},
booktitle={Proc. ICML},
year = {2003}
} 

@book{Cressie2011,
  title = 	 {Statistics for Spatio-Temporal Data},
  author = 	 {N. Cressie and C. K. Wikle},
  publisher = {Wiley Series in Probability and Statistics},
  year = 	 {2011}
 } 

@book{Cressie1991,
  title = 	 {Statistics for Spatial Data},
  author = 	 {N. Cressie},
  publisher = {Wiley Series in Probability and Statistics},
  year = 	 {1991}
 } 

@INPROCEEDINGS{Asmuth2011,
AUTHOR = "J. Asmuth and M. L. Littman",
TITLE = "Learning is planning: Near {Bayes}-optimal reinforcement learning via {Monte-Carlo} tree search",
BOOKTITLE = "Proc. UAI",
PAGES = {19-26},
YEAR = {2011}, 
}

@Inproceedings{Mauricio2012,
author = "M. {A.-Lopez} and V. Thomas and O. Buffet",
title = "Near-Optimal {BRL} using Optimistic Local Transitions",
booktitle = "Proc. ICML",
year = {2012} 
}

@phdthesis{Castro07PhDThesis,
  author = 	 {R. Castro},
  title = 	 {Active Learning and Adaptive Sampling for Non-Parametric Inference},
  school = 	 {Rice University},
  year = 	 {2007},
  address = 	 {Houston, Texas},
  month = 	 {August}
}

@phdthesis{Low09PhDThesis,
  author = 	 {K. H. Low},
  title = 	 {Multi-Robot Adaptive Exploration and Mapping for Environmental Sensing Applications},
  school = 	 {Carnegie Mellon University},
  year = 	 {2009},
  month = 	 {Summer}
}

@article{Tsybakov99,
author = "E. Mammen and A. B. Tsybakov",
title = "Smooth Discrimination Analysis",
journal = "The Annals of Statistics",
year = {1999},
volume = "27",
number = "6",
pages = {1808-1829}
}

@inproceedings{Castro05,
author = {R. Castro and R. Willett and R. Nowak},
title = "Faster Rate in Regression via Active Learning",
booktitle = "Proc. NIPS",
year = "2005"
}

@article{Balcan09,
author = {M.-F. Balcan and A. Beygelzimer and J. Langford},
title = "Agnostic Active Learning",
journal = " J. Comput. Syst. Sci.",
year = "2009",
volume = "75",
number = "1",
pages = {78--89}
}

@inproceedings{Balcan06,
author = {M.-F. Balcan and A. Beygelzimer and J. Langford},
title = "Agnostic Active Learning",
booktitle = "Proc. ICML",
year = "2006"
}

@inproceedings{Hanneke07,
author = {S. Hanneke},
title = "A Bound on the Label Complexity of Agnostic Active Learning",
booktitle = "Proc. ICML",
pages = "353--360",
year = "2007"
}

@inproceedings{Hanneke08,
author = {M.-F. Balcan and S. Hanneke and J. Wortman},
title = "The True Sample Complexity of Active Learning",
booktitle = "Proc. COLT",
year = "2008"
}

@inproceedings{Liu11,
author = {L. Yang and S. Hanneke and J. Carbonell},
title = "The Sample Complexity of Self-Verifying {Bayesian} Active Learning",
booktitle = "Proc. AISTATS",
pages = "816--822",
year = "2011"
}

@inproceedings{Dasgupta07,
author = {S. Dasgupta and D. Hsu and C. Monteleoni},
title = "A general agnostic active learning algorithm",
booktitle = "Proc. NIPS",
year = "2007"
}

@inproceedings{Beygelzimer09,
author = {A. Beygelzimer and S. Dasgupta and J. Langford},
title = "Importance Weighted Active Learning",
booktitle = "Proc. NIPS",
year = "2009"
}

@inproceedings{Wang09,
author = {L. Wang},
title = "Sufficient Conditions for Agnostic Active Learnable",
booktitle = "Proc. NIPS",
year = "2009"
}

@inproceedings{Friedman09,
author = {E. J. Friedman},
title = "Active Learning for Smooth Problems",
booktitle = "Proc. ICML",
year = "2009"
}

@inproceedings{Park12,
author = {M. Park and J. W. Pillow},
title = "{Bayesian} active learning with localized priors for fast receptive field characterization",
booktitle = "Proc. NIPS",
pages = "2357--2365",
year = "2012"
}

@inproceedings{Houlsby12,
author = "N. Houlsby and J. M. {H.-Lobato} and F. Huszar and Z. Ghahramani",
title = "Collaborative {Gaussian} Processes for Preference Learning",
booktitle = "Proc. NIPS",
pages = "2105--2113",
year = "2012"
}

@inproceedings{Azami10,
author = {J. Azami and X. Fern and A. Fern and E. Burrows and F. Chaplen and Y. Fan and H. Liu and J. Jiao and R. Schaller},
title = "Myopic Policies for Budgeted Optimization with Constrained Experiments",
booktitle = "Proc. AAAI",
year = "2010"
}

@article{Zacks69,
author = {S. Zacks},
title = "Bayes Sequential Designs of Fixed Size Samples from Finite Populations",
journal = "Journal of the American Statistical Association",
volume = "64",
number = "328",
pages = {1342-1349},
month = {December},
year = "1969"
}

@article{Zacks70,
author = {H. Solomon and S. Zacks},
title = "Optimal Design of Sampling from Finite Populations: A Critical Review and Indication of New Research Areas",
journal = "J. American Statistical Association",
volume = "65",
number = "330",
pages = "653--677",
year = "1970"
}

@article{Brockwell03,
author = {A. E. Brockwell and J. B. Kadane},
title = "A Gridding Method for Bayesian Sequential Decision Problems",
journal = "Journal of Computational and Graphical Statistics",
volume = "12",
number = "3",
pages = {566-584},
year = "2003"
}

@article{Muller07,
author = {P. Muller and D. A. Berry and A. P. Grieve and M. Smith and M. Krams},
title = "Simulation-based sequential Bayesian design",
journal = "Journal of Statistical Planning and Inference",
volume = "137",
pages = {3140-3150},
year = "2007"
}

@inproceedings{Pal05,
author = {C. Chekuri and M. Pal},
title = {A Recursive Greedy Algorithm for Walks in Directed Graphs},
booktitle = "Annual Symposium on Foundation of Computer Science",
pages = {245-253},
year = "2005"
}

@article{Singh09,
author = {A. Singh and A. Krause and C. Guestrin and W. J. Kaiser},
title = "Efficient Informative Sensing using Multiple Robots",
journal = "J. Artificial Intelligence Research",
volume = "34",
pages = "707--755",
year = "2009"
}

@inproceedings{Meliou07,
author = {A. Meliou and A. Krause and C. Guestrin and J. M. Hellerstein},
title = "Nonmyopic Informative Path Planning in Spatio-Temporal Models",
booktitle = "Proc. AAAI",
pages = "602--607",
year = "2007"
}

@inproceedings{Geoffrey13,
author = {G. A. Hollinger and G. S. Sukhatme},
title = "Stochastic Motion Planning for Robotic Information Gathering",
booktitle = "Proc. RSS",
year = "2013"
}

@inproceedings{Lan13,
author = {X. Lan and M. Schwager},
title = "Planning Periodic Persistent Monitoring Trajectories for Sensing Robots in Gaussian Random Fields",
booktitle = "Proc. ICRA",
pages = {2407-2412},
month = "May",
year = "2013"
}

@article{Emery98,
author = {A. F. Emery and Aleksey V. Nenarokomov},
title = "Optimal Experiment Design",
journal = "Measurement Science and Technology",
volume = "9",
number = "6",
year = "1998"
}

@article{Cohn96,
author = {David A. Cohn and Zoubin Ghahramani and Michael I. Jordan},
title = "Active Learning with Statistical Models",
journal = "Journal of Artificial Intelligence Research",
volume = "4",
pages = "129--145",
year = "1996"
}

@inproceedings{Low08,
author = {K. H. Low and John M. Dolan and Pradeep Khosla},
title = "Adaptive Multi-Robot Wide-Area Exploration and Mapping",
booktitle = "Proc. AAMAS",
month = "May",
year = "2008"
}

@inproceedings{Low09,
author = {K. H. Low and John M. Dolan and Pradeep Khosla},
title = "Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing",
booktitle = "Proc. ICAPS",
year = "2009"
}

@article{Guestrin08,
   author    = "A. Krause and A. Singh and C. Guestrin",
   title     = "Near-Optimal Sensor Placements in {Gaussian} Processes: Theory, Efficient Algorithms and Empirical Studies",
   journal   = "JMLR",
   volume    = 9,
   pages     = "235--284",
   year      = 2008 }
   
@inproceedings{Andreas07,
author = {A. Krause and C. Guestrin},
title = "Nonmyopic active learning of {Gaussian} processes: An exploration{-}exploitation approach",
booktitle = "Proc. ICML",
pages = "449--456",
year = "2007"
}

@inproceedings{Golovin10,
author = {D. Golovin and A. Krause and D. Ray},
title = "Near-Optimal {Bayesian} Active Learning with Noisy Observations",
booktitle = "Proc. NIPS",
pages = "766--774",
year = "2010"
}

@book{Rasmussen06,
   author    = "C. E. Rasmussen and C. K. I. Williams",
   title     = "Gaussian Processes for Machine Learning",
   publisher = "MIT Press",
   year      = 2006 }
   
@article{Leonard07,
author = "N. E. Leonard and D. A. Palley and F. Lekien and R. Sepulchre and D. M. Fratantoni and R. E. Davis",
title = "Collective Motion, Sensor Networks, and Ocean Sampling",
journal = "Proc. IEEE",
volume = 95,
number = 1,
pages = "48--74",
year = "2007"
}

@article{Valiant84,
author = {L. G. Valiant},
title = "A theory of the learnable",
journal = "Communications of the ACM",
volume = "27",
number = "11",
pages = "1134--1142",
month = "November",
year = "1984"
}

@article{Martin01,
author = {R. J. Martin},
title = "Comparing and contrasting some environmental and experimental design problems",
journal = "Environmetrics",
volume = "12",
number = 3,
pages = "303--317",
year = "2001"
}

@article{Diggle06,
author = {P. J. Diggle},
title = "Bayesian Geostatistical Design",
journal = "Scand. J. Statistics",
volume = "33",
number = "1",
pages = "53-64",
year = "2006"
}

@article{Zimmerman06,
author = {D. L. Zimmerman},
title = "Optimal network design for spatial prediction, covariance parameter estimation, and empirical prediction",
journal = "Environmetrics",
volume = "17",
number = "6",
pages = "635-652",
year = "2006"
}

@book{Muller01,
  author = 	 "W. G. {M\"{u}ller}",
  title = 	 "Collecting Spatial Data: Optimum Design of Experiments for Random Fields",
  publisher = "Springer",
  edition = "3rd",
  year = 2007
 } 
 
 @article{Peter05,
author = {P. Stone and R. S. Sutton and G. Kuhlmann},
title = "Reinforcement Learning for RoboCup Soccer Keepaway",
journal = "International Society for Adaptive Behavior",
volume = "13",
number = "3",
pages = "165-188",
year = "2005"
}

@article{Riedmiller09,
author = {M. Riedmiller and T. Gabel and R. Hafner and S. Lange},
title = {Reinforcement Learning for Robot Soccer},
journal = {Automaton Robot},
volume = "27",
pages = "55-73",
year = "2009"
}

@inproceedings{Poupart08,
author = {P/ Poupart and N. Vlassis},
title = "Model-based Bayesian reinforcement learning in partially observable domains",
booktitle = "Proc. ISAIM",
year = "2008"
}

@inproceedings{Yi12,
author = {Y. Wang and K. S. Won and D. Hsu and W. S. Lee},
title = "Monte Carlo Bayesian Reinforcement Learning",
booktitle = "Proc. ICML",
year = "2012"
}

@inproceedings{Li10,
author = {L. Li and W. Chu and J. Langford and R. E. Schapire},
title = "A Contextual-Bandit Approach to Personalized News Article Recommendation",
booktitle = "Proceedings of the 19th International Conference on World Wide Web",
pages = "661-670",
year = "2010"
}

@inproceedings{Li09,
author = {L. Li and J. D. Williams and S. Balakrishnan},
title = "Reinforcement Learning for Spoken Dialog Management using Least-Squares Policy Iteration and Fast Feature Selection",
booktitle = "Proceedings of the 10th Annual Conference of the International Speech Communication Association",
pages = "2475-2478",
year = "2009"
}

@inproceedings{Stephane07,
author = {S. Ross and B. Chaib-draa and J. Pineau},
title = "Bayes-Adaptive POMDPs",
booktitle = "Proc. NIPS",
year = "2007"
}

@inproceedings{Stephane08,
author = {S. Ross and J. Pineau},
title = "Model-based Bayesian reinforcement learning in large structured domains",
booktitle = "Proc. UAI",
year = "2008"
}

@phdthesis{William06PhDThesis,
author = {J. D. Williams},
title = {Partially Observable Markov Decision Processes for Spoken Dialogue Management},
school = {Cambridge University},
year = {2006}
}

@inproceedings{Chen2012,
title = {Stream-based Joint Exploration-Exploitation Active Learning},
author = {C. C. Loy and T. M. Hospedales and T. X. and S. Gong},
booktitle = "Proc. {CVPR}",
year = 2012
}

@inproceedings{Yu2012,
title = {Hierarchical Bayesian Nonparametric Approach to Modeling and Learning the Wisdom of Crowds of Urban Traffic Route Planning Agents},
author = {J. Yu and K. H. Low and A. Oran and P. Jaillet},
booktitle = "Proc. {IAT}",
year = 2012
}

@inproceedings{LowAAAI14,
	author = "N. Xu and K. H. Low and J. Chen and K. K. Lim and E. B. {\"{O}zg\"{u}l}",
	title ="{GP-Localize}: Persistent Mobile Robot Localization using Online Sparse {Gaussian} Process Observation Model",   
	booktitle = "Proc. {AAAI}",
	pages = "2585--2592",
	year = {2014}
}

@article{Opper02,
 author = "L. {Csat\'{o}} and M. Opper",
 title = "Sparse online {G}aussian processes",
 journal = "Neural Computation",
 volume = "14",
 number = "3",
 pages = "641--669",
 year = "2002"
 }
 
 @article{Candela05,
 author = "J. {Qui\~{n}onero}-Candela and C. E. Rasmussen",
 title = "A unifying view of sparse approximate {Gaussian} process regression",
 journal = "Journal of Machine Learning Research",
 volume = "6",
 pages = "1939--1959",
 year = "2005"
 }
 
 @article{Candela07,
 author = "J. {Qui\~{n}onero}-Candela and C. E. Rasmussen and C. K. I. Williams",
 title = "Approximation methods for Gaussian process regression",
 journal = "Large-Scale Kernel Machines",
 pages = "203--223",
 year = "2007"
 }
 
 
 @inproceedings{Seeger03,
author = {M. Seeger and C. K. I. Williams and N. D. Lawrence},
title = "Fast forward selection to speed up sparse {Gaussian} process regression",
booktitle = "Proc. AISTATS",
year = "2003"
}

@inproceedings{Tresp03,
author = {A. Schwaighofer and V. Tresp},
title = "Transductive and inductive methods for approximate {Gaussian} process regression",
booktitle = "Proc. NIPS",
pages = "953--960",
year = "2003"
}

@inproceedings{Smola01,
author = {A. J. Smola and P. L. Bartlett},
title = "Sparse greedy {G}aussian process regression",
booktitle = "Proc. NIPS",
pages = "619--625",
year = "2001"
}

@inproceedings{Snelson06,
author = {E. Snelson and Z. Gharahmani},
title = "Sparse {G}aussian processes using pseudo-inputs",
booktitle = "Proc. NIPS",
pages = "1259--1266",
year = "2005"
}

@inproceedings{Titsias09,
author = {M. K. Titsias},
title = "Variational learning of inducing variables in sparse {G}aussian processes",
booktitle = "Proc. {AISTATS}",
year = "2009"
}

@article{Tresp00,
 author = "V. Tresp",
 title = "A {B}ayesian committee machine",
 journal = "Neural Computation",
 volume = "12",
 pages = "2719--2741",
 year = "2000"
 }
 
@inproceedings{Hensman13,
author = {J. Hensman and N. Fusi and N. D. Lawrence},
title = "Gaussian processes for big data",
booktitle = "Proc. UAI",
pages = "282--290",
year = "2013"
}

@inproceedings{Chen13,
author = {J. Chen and N. Cao and K. H. Low and R. Ouyang and C. K.-Y. Tan and P. Jaillet},
title = "Parallel {G}aussian process regression with low-rank covariance matrix approximations",
booktitle = "Proc. UAI",
pages = "152-161",
year = "2013"
}

@article{Miguel10,
 author = "M. {L\'{a}zaro}-Gredilla and J. {Qui\~{n}onero}-Candela and C. E. Rasmussen and A. R. Figueiras-Vidal",
 title = "Sparse spectrum {G}aussian process regression",
 journal = "Journal of Machine Learning Research",
 pages = "1865--1881",
 year = "2010"
 }
 
 @inproceedings{Hensman12,
 author = {J. Hensman and M. Rattray and N. D. Lawrence},
 title = "Fast Variational Inference in the Conjugate Exponential Family",
 booktitle = "Proc. NIPS",
 year = "2012"
 }
 
 @inproceedings{Snelson07a,
 author = {E. L. Snelson and Z. Ghahramani},
 title = "Local and global sparse {G}aussian process approximation",
 booktitle = "Proc. AISTATS",
 year = "2007"
 }

@article{Amari98,
 author = "S. Amari",
 title = "Natural Gradient Works Efficiently in Learning",
 journal = "Neural Computation",
 pages = "251--276",
 volume = "10",
 year = "1998"
 }

@article{Hoffman13,
 author = "M. D. Hoffman and D. M. Blei and C. Wang and J. Paisley",
 title = "Stochastic Variational Inference",
 journal = "Journal of Machine Learning Research",
 pages = "1303--1347",
 year = "2013"
 }

@phdthesis{Snelson07,
  author = "E. L. Snelson",
  title = "Flexible and efficient {Gaussian} process models for machine learning",
   type      = "{Ph.D. Thesis}",	
      address   = "London, UK",
  school = "University College London",
  year = {2007}
}

@article{Monro1951,
 title = "A stochastic approximation method",
 author = "H. Robbins and S. Monro",
   journal = "Ann. Math. Statist.",
   volume = 22,
   number = 3,
   pages     = "400--407",
   year      = 1951
 } 

@book{Bishop06,
   author = "C. M. Bishop",
   title = "{Pattern Recognition} and {Machine Learning}",
   publisher = "Springer",
   year = 2006
}

@inproceedings{NghiaICML14,
 title = "Nonmyopic $\epsilon$-{B}ayes-Optimal Active Learning of {G}aussian Processes",
 author = "T. N. Hoang and K. H. Low and P. Jaillet and M. Kankanhalli",
   booktitle = "Proc. ICML",
   pages     = "739--747",
   year      = 2014
 } 

@article{Vijayakumar05,
 author = "S. Vijayakumar and A. D{'}Souza and S. Schaal",
 title = "Incremental online learning in high dimensions",
 journal = "Neural Computation",
 pages = "2602--2634",
 volume = "17",
 number = "12",
 year = "2005"
 }
 
 @inproceedings{Steven06,
 author = "S. C. H. Hoi and R. Jin and J. Zhu and M. R. Lyu",
 title = "Batch Mode Active Learning and Its Application to Medical Image Classification",
 booktitle = "Proc. ICML",
 year = "2006"
 }
 
 @inproceedings{Yuxin13,
 author = "Y. Chen and A. Krause",
 title = "Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization",
 booktitle = "Proc. ICML",
 year = "2013"
 }
 
 @inproceedings{Sabou14,
 author = "M. Sabou and K. Bontchenva and L. Derczynski and A. Scharl",
 title = "Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines",
 booktitle = "Proc. LREC",
 pages = "859--866",
 year = "2014"
 }
 
 @inproceedings{Kemple03,
 author = "D. Kemple and J. Kleinberg and E. Tardos",
 title = "Maximizing the spread of influence through a social network",
 booktitle = "Proc. KDD",
 pages = "137--146",
 year = "2003"
 }
 
 @article{Anshelevich09,
 author = "E. Anshelevich and D. Chakrabarty and A. Hate and C. Swamy",
 title = "Approximation algorithms for the firefighter problem: Cuts over time and submodularity",
 journal = "Algorithms and Computation",
 publisher = "Springer Berlin / Heidelberg",
 year = "2009"
 }

@article{Golovin11,
 author = "D. Golovin and A. Krause",
 title = "Adaptive submodularity: Theory and applications in active learning and stochastic optimization",
 journal = "Journal of Artificial Intelligence Research",
 pages = "427--486",
 volume = "42",
 year = "2011"
 }
 
 @inproceedings{Cuong13,
 author = "N. V. Cuong and W. S. Lee and N. Ye and K. M. A. Chai and H. L. Chieu",
 title = "Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion",
 booktitle = "Proc. NIPS",
 year = "2013"
 }
 
 @inproceedings{Cuong14,
 author = "N. V. Cuong and W. S. Lee and N. Ye",
 title = "Near-optimal Adaptive Pool-based Active Learning with General Loss",
 booktitle = "Proc. UAI",
 year = "2014"
 }
 
 @inproceedings{Mauricio10,
 author = "M. A. \'{A}lvarez and D. Luengo and M. K. Titsias and N. D. Lawrence",
 title = "Efficient Multioutput {G}aussian Processes through Variational Inducing Kernels",
 booktitle = "Proc. AISTATS",
 pages = "25--32",
 year = "2010"
 }

@inproceedings{Phillip05,
 author = "P. Boyle and M. Frean",
 title = "Dependent {G}aussian Processes",
 booktitle = "Proc. NIPS",
 year = "2005"
 }

@inproceedings{Bonilla08,
 author = "E. V. Bonilla and K. M. A. Chai and C. K. I. Williams",
 title = "Multi-task {G}aussian Process Prediction",
 booktitle = "Proc. NIPS",
 year = "2008"
 }

@inproceedings{Yarin14,
 author = "Y. Gal and M. {van der Wilk} and C. Rasmussen",
 title = "Distributed Variational Inference in Sparse {G}aussian Process Regression and Latent Variable Models",
 booktitle = "Proc. NIPS",
 year = "2014"
 } 
 
 @article{Dave02,
 author = "D. Higdon",
 title = "Space and Space-Time Modeling using Process Convolutions",
 journal = "Journal of Quantitative Methods for Current Environmental Issues",
 pages = "37--56",
 year = "2002"
 }
 
 @article{Ansell06,
   author = "T. J. {Ansell et al.}",
   title = "Daily Mean Sea Level Pressure Reconstructions for the {European-North Atlantic} Region for the Period $1850$-$2003$",
   journal = "J. Climate",
   volume = 19,
   number = 12,
   pages = "2717--2742",
   year = 2006 }

@article{LowAURO19,
author = {O. Ruofei and K. H. Low},
year = {2019},
month = {01},
pages = {1-18},
title = {Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception},
journal = {Autonomous Robots},
doi = {10.1007/s10514-018-09826-z}
}
   
 @inproceedings{LowAAAI15,
   author    = "K. H. Low and J. Yu and J. Chen and P. Jaillet",
   title     = "Parallel {Gaussian} Process Regression for Big Data: Low-Rank Representation Meets {M}arkov Approximation",
   booktitle = "Proc. {AAAI}",
   year      = 2015,
   pages = "2821--2827"

}

 @inproceedings{LowAAAI18,
   author    = "O. Ruofei and K. H. Low",
   title     = "Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception",
   booktitle = "Proc. {AAAI}",
   year      = 2018,
   pages = "3876--3883"
}
   
 @inproceedings{Jit10,
   author    = "K. Jitvanichphaibool and Y-C Liang and Y. Zeng",
   title     = "Spectrum Sensing Using Multiple Antennas for Spatially and Temporally Correlated Noise Environments",
   booktitle = "Proc. {IEEE} Symposium on New Frontiers in Dynamic Spectrum",
   page = "1-7",
   year      = 2010 }  
   
 @article{Ali15,
 author = "A. Koochakzadeh and M. {Malek-Mohammadi} and M. {Babaie-Zadeh} and M. {Skoglund}",
 title = "Multi-antenna assisted spectrum sensing in spatially correlated noise environments",
 journal = "Signal Processing",
 volume = "108",
 pages = "69--76",
 year = "2015"
 }

@article{Huizenga95,
author = "H. M. Huizenga and P. C. M. Molenaar",
title = "Equivalent source estimation of scalp potential fields contaminated by heteroscedastic and correlated noise",
journal = "Brain Topography",
volume = "8",
number = "1",
pages = "13--33",
year = "1995"
}

@book{Caiti06,
   author = "A. Caiti and N. R. Chapman and J.-P. Hermand and S. M. Jesus",
   title = "Acoustic Sensing Techniques for the Shallow Water Environment: Inversion Methods and Experiments",
   publisher = "Springer",
   year = 2006
}
 
 @book{Daphne09,
   	author = "D. Koller and N. Friedman",
   	title = "Probabilistic Graphical Models: Principles and Techniques",
    publisher = "MIT Press",
    year = 2009
 } 
   
 @techreport{Titsias09a,
 title = "Variational Model Selection for Sparse {Gaussian} Process Regression",
 author = "M. K. Titsias",
 institution = {School of Computer Science, University of Manchester},
 year = {2009}
 }
 
 @inproceedings{Kersting07,
 title = "Most Likely Heteroscedastic {Gaussian} Process Regression",
 author = "K. Kersting and C. Plagemann and P. Pfaff and W. Burgard",
 booktitle = "Proc. {ICML}",
 pages  = "393--400",
 year = {2007}
 }
 
 @inproceedings{Quoc05,
 title = "Heteroscedastic {Gaussian} Process Regression",
 author = "Q. V. Le and A. J. Smola and S. Canu",
 booktitle = "Proc. {ICML}",
 year = {2005}
 }
 
 @inproceedings{Titsias11,
 title = "Variational Heteroscedastic {Gaussian} Process Regression",
 author = "M. {L\'{a}zaro}-Gredilla and M. K. Titsias",
 booktitle = "Proc. {ICML}",
 pages = "841--848",
 year = {2011}
 }
 
 @inproceedings{Goldberg98,
 title = "Regression with input-dependent noise: A {G}aussian process treatment",
 author = "P. W. Goldberg and C. K. I. William and C. M. Bishop",
 booktitle = "Proc. {NIPS}",
 pages = {493--499},
 year = {1997}
 }
 
 @inproceedings{NghiaICML15,
 title = "A Unifying Framework of Anytime Sparse {Gaussian} Process Regression Models with Stochastic Variational Inference for Big Data",
 author = "T. N. Hoang and Q. M. Hoang and K. H. Low",
 booktitle = "Proc. {ICML}",
 pages = "569--578",
 year = {2015}
 }
 
 @inproceedings{Marc15,
 title = "Distributed {Gaussian} Processes",
 author = "M. P. Deisenroth and J. W. Ng",
 booktitle = "Proc. {ICML}",
 year = {2015},
 pages="1481--1490" 
 }
 
 @inproceedings{Smith01,
 title = "{G}aussian process priors with {ARMA} noise models",
 author = "R. Murray-Smith and A. Girard",
 booktitle = "Proc. {Irish Signals Systems Conference}",
 pages = "147-153",
 year = {2001}
 }
 
 @article{Moura05,
 title = "Block matrices with {L}-block-banded inverse: Inversion algorithms",
 author = "A. Asif and J. M. F. Moura",
 journal = "IEEE Trans. Signal Processing",
 pages = "630-642",
 volume = "53",
 number = "2",
 year = {2005}
 }
 
 @article{Brooke01,
 title = "Benchmarks and the accuracy of {GARCH} model estimation",
 author = "C. Brooks and S. P. Burke and G. Persand",
 journal = "International Journal of Forecasting",
 pages = "45-56",
 volume = "17",
 year = {2001}
 }
 
 @article{Mockus91,
 title = "{B}ayesian Approach to Global Optimization and Application to Multiobjective and Constrained Problems",
 author = "J. B. Mockus and L. J. Mockus",
 journal = "J. Optimization Theory and Applications",
 volume = "70",
 number = "1",
 pages = "157--172",
 year = {1991}
 }
 
 @article{Peter03,
 title = "Using Confidence Bounds for Exploitation-Exploration Trade-Offs",
 author = "P. Auer",
 journal = "JMLR",
  pages = "397--422",
 volume = "3",
 year = {2002}
 } 
 
 @inproceedings{Srinivas10,
 title = "{G}aussian Process Optimization in the Bandit Setting: No Regret and Experimental Design",
 author = "N. Srinivas and A. Krause and S. Kakade and M. Seeger",
 booktitle = "Proc. {ICML}",
 pages="1015--1022",
 year = {2010}
 }
 
 @inproceedings{Kandasamy15,
 title = "High Dimensional {B}ayesian Optimization and Bandits via Additive Models",
 author = "K. Kandasamy and J. Schneider and B. P\'{o}czos",
 booktitle = "Proc. {ICML}",
 year = {2015}
 }
 
 @techreport{Kandasamy15a,
 title = "High-dimensional {B}ayesian Optimization and Bandits via Additive Models",
 author = "K. Kandasamy and J. Schneider and B. P\'{o}czos",
 type     = "{arXiv:1503.01673}",
 year = {2016}
 }
 
 @inproceedings{Zi17,
 title = "Batched High-dimensional {B}ayesian Optimization via Structural Kernel Learning",
 author = "Z. Wang and C. Li and S. Jegelka and P. Kohli",
 booktitle = "Proc. {ICML}",
 pages = "3656--3664",
 year = {2017}
 }
 
 @inproceedings{Snoek12,
   author = "J. Snoek and L. Hugo and R. P. Adams",
   title = "Practical {B}ayesian Optimization of Machine Learning Algorithms",
   booktitle = "Proc. {NIPS}",
   pages = "2960--2968",
   year = {2012}
 }
 
 @inproceedings{Yamins13,
   author = "J. Bergstra and D. Yamins and D. D. Cox",
   title = "Making a Science of Model Search: Hyperparameter Optimization and Hundreds of Dimensions for Vision Architectures",
   booktitle = "Proc. {ICML}",
   pages = "115--123",
   year = {2013}
 }
 
 @inproceedings{Wang13,
   author = "Z. Wang and M. Zoghi and F. Hutter and D. Matheson and N. {de Freitas}",
   title = "{B}ayesian Optimization in High Dimensions via Random Embeddings",
   booktitle = "Proc. {IJCAI}",
   pages = "1778--1784",
   year = {2013}
 }
 
 @inproceedings{Gonzalez14,
   author = "J. Gonz{\'{a}}lez and J. Longworth and D. James and N. Lawrence",
   title = "Bayesian Optimization for Synthetic Gene Design",
   booktitle = "NIPS Workshop on {B}ayesian Optimization in Academia and Industry",
   year = {2014}
 }
 
 @inproceedings{Lizotte07,
   author = "D. Lizotte and T. Wang and M. Bowling and D. Schuurmans",
   title = "Automatic Gait Optimization with {G}aussian Process Regression",
   booktitle = "Proc. {IJCAI}",
  pages = "944--949",
   year = {2007}
 }
 
 @inproceedings{Lobato14,
   author = "J. M. Hern\'{a}ndez-Lobato and M. W. Hoffman and Z. Ghahramani",
   title = "Predictive Entropy Search for Efficient Global Optimization of Black-Box Functions",
   booktitle = "Proc. {NIPS}",
   pages = "918--926",
   year = {2014}
 }
 
 @inproceedings{Yehong16,
   author = "Y. Zhang and T. N. Hoang and K. H. Low and M. Kankanhalli",
   title = "Near-Optimal Active Learning of Multi-Output {G}aussian Processes",
   booktitle = "Proc. {AAAI}",
   pages = "2351-2357",
   year = {2016}
 }
 
@incollection{Mockus78,
   author = "J. Mockus and V. Tie{\v{s}}is and A. {\v{Z}}ilinskas",
   title = "The application of {B}ayesian methods for seeking the extremum",
   editor = "L. C. W. Dixon and G. P. Szeg{\v{o}}",
   booktitle = "Towards Global Optimization 2",
   pages = "117--128",
   publisher = "North-Holland Publishing Company",
   year = {1978}
 }
 
 @article{Hennig12,
   author    = "P. Hennig and C. J. Schuler",
   title     = "Entropy Search for Information-Efficient Global Optimization",
   journal   = "JMLR",
   volume    = 13,
   pages     = "1809--1837",
   year      = 2012 }
 
 @article{Gelman05,
   author = "A. Gelman",
   title = "Analysis of variance: Why is it more important than ever (with discussion)",
   journal = "{A}nnals of {S}tatistics",
   volume = "33",
   pages = "1-53",
   year = {2005}
 }
 
 @inproceedings{Hornby06,
   author = "G. S. Hornby and A. Globus and D. S. Linden and J. D. Lohn",
   title = "Automated Antenna Design with Evolutionary Algorithms",
   booktitle = "Proc. {AIAA} Space Conference",
   year = {2006}
 }

@inproceedings{NghiaICRA18,
 	author = "T. N. Hoang and Y. Xiao and K. Sivakumar and C. Amato and J. How",
 	title = "Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems",
 	booktitle = "Proc. {ICRA}",
 	year = {2018}
 }
 
 @inproceedings{NghiaAAAI17,
   author = "Q. M. Hoang and T. N. Hoang and K. H. Low",
   title = "A Generalized Stochastic Variational {B}ayesian Hyperparameter Learning Framework for Sparse Spectrum {G}aussian Process Regression",
   booktitle = "Proc. {AAAI}",
   pages = "2007--2014",
   year = {2017}
 }
 
 @inproceedings{Djolonga13,
   author = "J. Djolonga and A. Krause and V. Cevher",
   title = "High-Dimensional {G}aussian Process Bandits",
   booktitle = "Proc. {NIPS}",
   pages = "1025--1033",
   year = {2013}
 } 
 
 @inproceedings{NghiaECMLKDD14,
 	author = "T. N. Hoang and K. H. Low and P. Jaillet and M. S. Kankanhalli",
 	title = "Active learning is planning: Non-myopic $\epsilon$-{B}ayes-optimal active learning of {G}aussian processes",
 	booktitle = "Proc. {ECML-PKDD Nectar Track}",
 	pages = "494--498",
 	year = {2014}
 }
 
 @inproceedings{NghiaICML19b,
    author = "M. Yurochkin and M. Agarwal and S. Ghosh and K. Greenewald and T. N. Hoang and Y. Khazaeni",
    title = "Bayesian Nonparametric Federated Learning of Neural Networks",
    booktitle = "Proc. ICML",
    year = {2019}
 }
 
 @inproceedings{NghiaAAAI18,
    author = "T. N. Hoang and Q. M. Hoang and O. Ruofei and K. H. Low",
    title = "Decentralized High-Dimensional Bayesian Optimization with Factor Graphs",
    booktitle = "Proc. AAAI",
    year = {2018}
 }

 @inproceedings{NatarajanICDSC14,
 	author = "P. Natarajan and T. N. Hoang and Y. Wong and K. H. Low and M. S. Kankanhalli",
 	title = "Scalable Decision-Theoretic Coordination and Control for Real-Time Active Multi-camera Surveillance",
 	booktitle = "Proc. {ICDSC}",
 	pages = "115-120",
 	year = {2014}
 }
 
@inproceedings{Ali17,
author = {Yahya, Ali and Li, Adrian and Kalakrishnan, Mrinal and Chebotar, Yevgen and Levine, Sergey},
year = {2017},
month = {09},
pages = {79-86},
title = {Collective robot reinforcement learning with distributed asynchronous guided policy search},
doi = {10.1109/IROS.2017.8202141},
booktitle = {International Conference on Intelligent Robots and Systems}
}
 
 @article{Wang16,
   author = "Z. Wang and M. Zoghi and F. Hutter and D. Matheson and N. {de Freitas}",
   title = "{B}ayesian Optimization in a Billion Dimensions via Random Embeddings",
   journal = "JAIR",
   volume = "55",
   pages = "361--387",
   year = {2016}
 }

@article{Klemen12,
   author = "K. Naver{\v{s}}nik and K. Rojnik",
   title = "Handling Input Correlations in Pharmacoeconomic Models",
   journal = "Value in Health",
   volume = "15",
   issue = "3",
   pages = "540--549",
   year = {2012}
 }
 
@inproceedings{McMahan17,
title	= {Communication-Efficient Learning of Deep Networks from Decentralized Data},
author	= {H. Brendan McMahan and Eider Moore and Daniel Ramage and Seth Hampson and Blaise Aguera y Arcas},
year	= {2017},
booktitle	= "Proc. {AISTATS}",
pages= "1273--1282"
}

@inproceedings{Singh19,
title	= {Model Fusion with Optimal Transport},
author	= {S. P. Singh and M. Jaggi},
year	= {2019},
URL	= {https://arxiv.org/abs/1910.05653}
}

@article{Claesen15,
author = "M. Claesen and F. D. Smet and J. A. K. Suykens and B. D. Moor",
title = "A robust ensemble approach to learn from positive and unlabeled data using svm base models",
journal = "Neurocomputing",
volume = "160",
pages = "73--84",
year = {2015}
}

@inproceedings{Zhang14,
title	= {Nonparallel hyperplane support vector machine for {PU} learning},
author	= {Y. Zhang and X. Ju and Y. Tian},
year	= {2014},
booktitle	= "Proc. {ICNC}",
pages= "703--708"
}

@inproceedings{Sugiyama15,
title	= {Convex formulation for learning from positive and unlabeled data},
author	= {M. Du Plessis and G. Niu and M. Sugiyama},
year	= {2015},
booktitle	= "Proc. {ICML}",
pages= "1386--1394"
}

@inproceedings{Ekan01,
title	= {The foundations of cost-sensitive learning},
author	= {C. Ekan},
year	= {2001},
booktitle	= "Proc. {IJCAI}",
pages= "1386--1394"
}

@inproceedings{weesun03,
title	= {Learning with positive and unlabeled examples using weighted logistic regression},
author	= {W. S. Lee and B. Liu},
year	= {2003},
booktitle	= "Proc. {ICML}",
pages= "448--455"
}

@inproceedings{weesun05,
title	= {A simple probabilistic approach to learning from positive and
unlabeled examples},
author	= {D. Zhang and W. S. Lee},
year	= {2005},
booktitle = "Proc. Annual UK Workshop on Computational Intelligence (UKCI)",
pages= "83--87"
}

@inproceedings{xliu03,
title	= {Learning to classify texts using positive and unlabeled data},
author	= {X. Liu and B. Liu},
year	= {2003},
booktitle	= "Proc. {IJCAI}",
pages= "587--592"
}

@inproceedings{xliu05,
title	= {Learning from positive and unlabeled examples with different data distributions},
author	= {X. Liu and B. Liu},
year	= {2005},
booktitle	= "Proc. {ECML}",
pages= "218--229"
}

@inproceedings{xliu07,
title	= {Learning to identify unexpected instances in the test set},
author	= {X. Liu and B. Liu and S. K. Ng},
year	= {2007},
booktitle	= "Proc. {IJCAI}",
pages= "2802--2807"
}

@inproceedings{xliu10,
title	= {Negative training data can be harmful to text classification},
author	= {X. Liu and B. Liu and S. K. Ng},
year	= {2010},
booktitle	= "Proc. {ACL}",
pages= "218--228"
}

@inproceedings{Finn17,
title	= {Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks},
author	= {C. Finn and P. Abbeel and S. Levine},
year	= {2017},
booktitle	= "Proc. {ICML}"
}

@inproceedings{Yoon18,
title	= {Baysian Model-Agnostic Meta-Learning},
author	= {J. Yoon and T. Kim and O. Dia and S. Kim and Y. Bengio and S. Ahn},
year	= {2018},
booktitle	= "Proc. {NeurIPS}"
}

@inproceedings{Matthieu19,
title	= {Disentangling Disentanglement in Variational Autoencoders},
author	= {E. Matthieu and T. Rainforth and N. Siddarth and Y. W. Teh},
year	= {2019},
booktitle	= "Proc. {ICML}"
}

@inproceedings{Kingma13,
author       = {Diederik P. Kingma and
                  Max Welling},
  title        = {Auto-Encoding Variational Bayes},
  year = {2014},
  booktitle    = {2nd International Conference on Learning Representations, {ICLR} 2014}
}

@inproceedings{NghiaIJCAI19a,
    author = "T. Fu and T. N. Hoang and C. Xiao and J. Sun",
    title = "DDL: Deep Dictionary Learning for Predictive Phenotyping",
    booktitle = "Proc. IJCAI",
    year = {2019}
 }

@inproceedings{NghiaIJCAI19b,
    author = "S. Hong and C. Xiao and T. N. Hoang and T. Ma and H. Li and J. Sun",
    title = "RDPD: Rich Data Helps Poor Data via Imitation",
    booktitle = "Proc. IJCAI",
    year = {2019}
 } 
 

@inproceedings{NghiaICML19a,
    author = "Q. M. Hoang and T. N. Hoang and K. H. Low and C. Kingsford",
    title = "Collective Model Fusion for Multiple Black-Box Experts",
    booktitle = "Proc. ICML",
    year = {2019}
 }

@article{Finlayson19,
author = "S. G. Finlayson and J. D. Bowers and J. Ito and J. L. Zittrain and A. L. Beam and I. S. Kohane",
title = "Adversarial Attacks on Medical Machine Learning",
journal = "Science",
volume = "363",
issue = "6433",
pages = "1287--1289",
year = {2019}
}

@article{lecun2010mnist,
  title={MNIST handwritten digit database},
  author={Y. LeCun and C. Cortes and CJ Burges},
  journal={ATT Labs [Online]. Available: http://yann. lecun. com/exdb/mnist},
  volume={2},
  year={2010}
}

@inproceedings{chen2016infogan,
  title={Infogan: Interpretable representation learning by information maximizing generative adversarial nets},
  author={X. Chen and Y. Duan and R. Houthooft and J. Schulman and I. Sutskever and P. Abbeel},
  booktitle={Proc. {NeurIPS}},
  pages={2172--2180},
  year={2016}
}

@inproceedings{mcallester1999pac,
	title={PAC-Bayesian model averaging},
	author={McAllester, David A},
	booktitle={Proceedings of the twelfth annual conference on Computational learning theory},
	pages={164--170},
	year={1999}
}

@inproceedings{Musco17,
	title={Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees},
	author={H. Avron and M. Kapralov and C. Musco and C. Musco and A. Velingker and A. Zandieh},
	booktitle={Proc. ICML},
	pages={253--262},
	year={2017}
}

@inproceedings{Rahimi07,
	title={Random features for large-scale kernel machines},
	author={A. Rahimi and B. Recht},
	booktitle={Proc. NIPS},
	year={2007}
}

@inproceedings{Musco16,
	title={Recursive Sampling for the Nystrom Method},
	author={C. Musco and C. Musco},
	booktitle={Proc. NIPS},
	year={2016}
}

@article{Hoeffding63,
	title={Probability inequalities for the sum of bounded random variables},
	author={W. Hoeffding},
	journal={Journal of the American Statistical Association},
	volume={58},
	pages={13--30},
	year={1963}
}

@article{Chernoff52,
	title={A measure of asymptotic efficiency for tests of hypothesis based on the sum of observations},
	author={H. Chernoff},
	journal={Annals of Mathematical Statistics},
	volume={23},
	pages={493--509},
	year={1952}
}

@book{shalev2014understanding,
	title={Understanding machine learning: From theory to algorithms},
	author={Shalev-Shwartz, Shai and Ben-David, Shai},
	year={2014},
	publisher={Cambridge university press}
}

@book{Mohri18,
	title={Foundations of Machine Learning},
	author={Mehryar Mohri and Afshin Rostamizadeh and Ameet Talwalkar},
	year={2018},
	publisher={MIT press}
}

@article{Choi12,
  title={Post-occupancy evaluation of 20 office buildings as basis for future IEQ standards and guidelines},
  author={J. H. Choi and V. Loftness and A. Aziz},
  journal={Energy and Buildings},
  volume={46},
  pages={167--175},
  year={2012}
}

@article{Maaten08,
  title={Visualizing High-Dimensional Data Using t-SNE},
  author={L. J. P. van der Maaten and G. E. Hinton},
  journal={Journal of Machine Learning Research},
  volume={9},
  pages={2579--2605},
  year={2008}
}

@misc{gas-data,
author = {Javier Burgues},
title = {
{Gas Sensor Array Temperature Modulation Dataset},
howpublished= {\url{https://archive.ics.uci.edu/ml/machine-learning-databases/00487/}}
}
}

@misc{abalone,
author = {Sam Waugh},
title = {
{Abalone Dataset},
howpublished= {\url{https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/}}
}
}

@article{burgues2018estimation,
  title={Estimation of the limit of detection in semiconductor gas sensors through linearized calibration models},
  author={Burgu{\'e}s, Javier and Jim{\'e}nez-Soto, Juan Manuel and Marco, Santiago},
  journal={Analytica chimica acta},
  volume={1013},
  pages={13--25},
  year={2018},
  publisher={Elsevier}
}


@article{burgues2018multivariate,
  title={Multivariate estimation of the limit of detection by orthogonal partial least squares in temperature-modulated MOX sensors},
  author={Burgu{\'e}s, Javier and Marco, Santiago},
  journal={Analytica chimica acta},
  volume={1019},
  pages={49--64},
  year={2018},
  publisher={Elsevier}
}

@inproceedings{Domeniconi2002,
	title={Adaptive nearest neighbor classification using support vector machines},
	author={C. Domeniconi and D. Gunopulos},
	booktitle={Proc. NIPS},
	year={2002}
}

@inproceedings{Peng2002,
	title={Adaptive kernel metric nearest neighbor classification},
	author={J. Peng and D. Heisterkamp and H. Dai},
	booktitle={Proc. ICPR},
	year={2002}
}

@inproceedings{Goldberger2005,
	title={Neighborhood components analysis},
	author={J. Goldberger and S. Roweis and G. Hinton and R. Salakhutdinov},
	booktitle={Proc. NIPS},
	year={2005}
}

@inproceedings{Xing2003,
	title={Distance metric learning with application to clustering with side-information},
	author={E. Xing and A. Ng and M. Jordan and S. Russell},
	booktitle={Proc. NIPS},
	year={2003}
}

@inproceedings{Lebanon2003,
	title={Flexible metric nearest neighbor classification},
	author={G. Lebanon},
	booktitle={Proc. IJCAI},
	year={2003}
}

@inproceedings{Weinshall2003,
	title={Learning distance functions using equivalence relations},
	author={A. Bar-Hillel and N. Shental and D. Weinshall},
	booktitle={Proc. ICML},
	year={2003}
}

@inproceedings{Chopra2005,
	title={Learning a similarity metric discriminatively with application to face verification},
	author={S. Chopra and R. Hadsell and Y. LeCun},
	booktitle={Proc. CVPR},
	pages={539-546},
	year={2005}
}

@article{Zhou2017,
  title={Deep Metric Learning for Visual Understanding: An Overview of Recent Advances},
  author={J. Lu and J. Hu and J. Zhou},
  journal={IEEE Signal Processing Magazine},
  volume={34},
  pages={76--84},
  year={2017}
}

@inproceedings{Davis2005,
	title={Information-Theoretic Metric Learning},
	author={J. V. Davis and P. Jain and S. Sra and I. S. Dhillon},
	booktitle={Proc. ICML},
	pages={209-216},
	year={2003}
}

@article{Duan2018,
  title={Deep Localized Metric Learning},
  author={J. Lu and J. Hu and J. Zhou},
  journal={IEEE Transaction on Circuit System and Video Technology},
  volume={28},
  pages={2644--2656},
  year={2018}
}

@inproceedings{Hoffer2017,
	title={Semi-supervised deep learning by metric embedding},
	author={E. Hoffer and N. Ailon},
	booktitle={Proc. ICLR},
	year={2017}
}

@article{Yang2018,
  title={Person Re-identification via Structural Deep Metric Learning},
  author={X. Yang and P. Zhou and M. Wang},
  journal={IEEE Transaction on Neural Network Learning System},
  pages={1--12},
  year={2018}
}

@book{Kulis2012,
	title={Metric Learning: A Survey},
	author={B. Kulis},
	year={2012},
	pages={287-364},
	publisher={Foundations and Trends in Machine Learning}
}

@article{Cortes1995,
  title={Support Vector Networks},
  author={C. Corinna and V. Vapnik},
  journal={Machine Learning},
  volume={20},
  issue={3},
  pages={273-297},
  year={1995}
}

@book{Goodfellow2016,
    title={Deep Learning},
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
    publisher={MIT Press},
    note={\url{http://www.deeplearningbook.org}},
    year={2016}
}

@inproceedings{Yeung2003,
	title={Parametric Distance Metric Learning with Label Information},
	author={Z. Zhang and J. Kwok and D. Yeung},
	booktitle={Proc. IJCAI},
	year={2003}
}

@inproceedings{Tsang2003,
	title={Learning with Idealized Kernels},
	author={J. T. Kwok and I. W. Tsang},
	booktitle={Proc. ICML},
	year={2003}
}

@inproceedings{Kwok2005,
	title={Applying neighborhood consistency for fast clustering and kernel density estimation},
	author={K. Zhang and M. Tang and J. T. Kwok},
	booktitle={Proc. CVPR},
	pages={1001-1007},
	year={2005}
}

@article{Hastie1996,
  title={Discriminant adaptive nearest neighbor classification},
  author={T. Hastie and R. Tibshirani},
  journal={IEEE Pattern Analysis and Machine Intelligence},
  volume={18},
  issue={6},
  year={1996}
}

@inproceedings{Hoang2020,
	title={Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes},
	author={Q. M. Hoang and T. N. Hoang and H. Pham and D. P. Woodruff},
	booktitle={Proc. NeurIPS},
	year={2020}
}

@inproceedings{Weinberger2006,
	title={Distance metric learning for large margin nearest neighbor classification},
	author={K. Weinberger and J. Blitzer and L. Saul},
	booktitle={Proc. NeurIPS},
	pages={1475-1482},
	year={2006}
}

@article{Tenenbaum2000,
  title={A global geometric framework for nonlinear dimensionality},
  author={J. Tenenbaum and V. Silva and J. C. Langford},
  journal={Science},
  volume={290},
  pages={2319-2323},
  year={2000}
}

@article{Saul2003,
  title={Think globally, fit locally: Unsupervised learning of low dimensional manifolds},
  author={L. K. Saul and S. T. Roweis},
  journal={JMLR},
  volume={4},
  pages={119-155},
  year={2003}
}

@article{Belkin2003,
  title={Laplacian eigenmaps for dimensionality reduction and data representation},
  author={M. Belkin and P. Niyogi},
  journal={Neural Computation},
  volume={15},
  pages={1373-1396},
  year={2003}
}

@article{Jordan2004,
  title={Learning the kernel matrix with semi-definite programming},
  author={G. R. G. Lanckriet and N. Cristianini and P. Bartlett and L. E. Ghaoui and M. I. Jordan},
  journal={JMLR},
  volume={5},
  pages={27-72},
  year={2004}
}

@inproceedings{Mateu2020,
	title={A Deep Metric Learning Method for Biomedical Passage Retrieval},
	author={A. Rosso-Mateu and M. Montes-y-Gome and F. A. Gonzale},
	booktitle={Proc. COLING},
	year={2020}
}

@inproceedings{Hoffer2015,
	title={Deep Metric Learning Using Triplet Network},
	author={E. Hoffer and N. Ailon},
	booktitle={Proc. ICLR Workshop},
	year={2015}
}

@inproceedings{Tan2015,
	title={Deep Transfer Metric Learning},
	author={J. Hu and J. Lu and Y-P Tan},
	booktitle={Proc. CVPR},
	year={2015}
}

@inproceedings{Karypis2011,
	author       = {Xia Ning and
                  George Karypis},
  title        = {{SLIM:} Sparse Linear Methods for Top-N Recommender Systems},
  year = {2011},
  booktitle    = {11th {IEEE} International Conference on Data Mining, {ICDM} 2011},
}

@article{harper2015movielens,
  title={The {MovieLens} datasets: History and context},
  author={F. M. Harper and J. A. Konstan},
  journal={{ACM} Transactions on Interactive Intelligent Systems (TIIS)},
  volume={5},
  number={4},
  pages={1--19},
  year={2015},
  publisher={ACM New York, NY, USA}
}

@inproceedings{sedhain2015autorec,
  title={Autorec: Autoencoders meet collaborative filtering},
  author={Sedhain, Suvash and Menon, Aditya Krishna and Sanner, Scott and Xie, Lexing},
  booktitle={Proc. WWW},
  pages={111--112},
  year={2015}
}

@inproceedings{he2017neural,
  title={Neural collaborative filtering},
  author={He, Xiangnan and Liao, Lizi and Zhang, Hanwang and Nie, Liqiang and Hu, Xia and Chua, Tat-Seng},
  booktitle={Proc. WWW},
  pages={173--182},
  year={2017}
}

@inproceedings{guo2017deepfm,
  title={DeepFM: a factorization-machine based neural network for CTR prediction},
  author={Guo, Huifeng and Tang, Ruiming and Ye, Yunming and Li, Zhenguo and He, Xiuqiang},
  booktitle={Proc. IJCAI},
  pages={1725--1731},
  year={2017}
}

@article{devlin2018bert,
  title={Bert: Pre-training of deep bidirectional transformers for language understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1810.04805},
  year={2018}
}

@article{koren2009matrix,
  title={Matrix factorization techniques for recommender systems},
  author={Koren, Yehuda and Bell, Robert and Volinsky, Chris},
  journal={Computer},
  volume={42},
  number={8},
  pages={30--37},
  year={2009},
  publisher={IEEE}
}

@article{Xiangnan2018,
  title={{NAIS:} {N}eural {A}ttentive {I}tem {S}imilarity {M}odel for {R}ecommendation},
  author={X. He and Z. He and J. Song and Z. Liu and Y-G. Jiang and T-S. Chua},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={30},
  issue={12},
  pages={2354--2366},
  year={2018},
  publisher={IEEE}
}

@inproceedings{Joonseok2018,
  title={Collaborative Deep Metric Learning for Video Understanding},
  author={J. Lee and S. Abu-El-Haija and B. Varadarajan and A. Natsev},
  booktitle={Proc. KDD},
  pages={481--490},
  year={2018}
}

@inproceedings{Li2019,
  author       = {Peng Hu and
                  Rong Du and
                  Yao Hu and
                  Nan Li},
  editor       = {Sarit Kraus},
  title        = {Hybrid Item-Item Recommendation via Semi-Parametric Embedding},
  year = {2019},
  booktitle    = {Proceedings of the Twenty-Eighth International Joint Conference on
                  Artificial Intelligence, {IJCAI} 2019},
}

@inproceedings{Hao2020,
  title={P-Companion: A Principled Framework for Diversified Complementary Product Recommendation},
  author={J. Hao and T. Zhao and J. Li and X. L. Dong and C. Faloutsos and Y. Sun and W. Wang},
  booktitle={Proc. CIKM},
  pages={2521--2527},
  year={2020}
}

@inproceedings{Salakhutdinov2007,
  title={Probabilistic Matrix Factorization},
  author={A. Mnih and R. Salakhutdinov},
  booktitle={Proc. NIPS},
  pages={1257--1264},
  year={2007}
}

@inproceedings{Salakhutdinov2008,
  title={Bayesian Probabilistic Matrix Factorization using Markov chain Monte Carlo},
  author={R. Salakhutdinov and A. Mnih},
  booktitle={Proc. ICML},
  pages={880--887},
  year={2008}
}

@inproceedings{Billsus2007,
  title={Content-based recommendation systems},
  author={M. J. Pazzani and D. Billsus},
  booktitle={Proc. The Adaptive Web, Methods and Strategies of Web Personalization},
  pages={325--341},
  year={2007}
}

@inproceedings{Roy2000,
  title={Content-based book recommending using learning for text categorization},
  author={R. J. Mooney and L. Roy},
  booktitle={Proc. 5th ACM Conference on Digital Libraries},
  pages={195--204},
  year={2000}
}

@inproceedings{Yu2009,
  title={Large-scale collaborative prediction using a non-parametric random effects model},
  author={K. Yu and J. Lafferty and S. Zhu and Y. Gong},
  booktitle={Proc. ICML},
  pages={1185--1192},
  year={2009}
}

@inproceedings{Lops2011,
  title={Content-based Recommender Systems: State of the art and trends},
  author={P. Lops and M. D. Gemmis and G. Semeraro},
  booktitle={Recommender Systems Handbook},
  pages={73--105},
  year={2011}
}

@inproceedings{Blei2011,
  title={Collaborative Topic Modeling for Recommending Scientific Articles},
  author={C. Wang and D. M. Blei},
  booktitle={Proc. KDD},
  pages={448--456},
  year={2011}
}

@inproceedings{Nickel2011,
  title={A three-way model for collective learning on multi-relational data},
  author={M. Nickel and V. Tresp and H. P. Kriegel},
  booktitle={Proc. ICML},
  pages={809--816},
  year={2011}
}

@inproceedings{Gordon2008,
  title={Relational learning via collective matrix factorization},
  author={A. P. Singh and G. J. Gordon},
  booktitle={Proc. KDD},
  pages={650--658},
  year={2008}
}

@inproceedings{Kanna19,
 author = {Xu, Da and Ruan, Chuanwei and Korpeoglu, Evren and Kumar, Sushant and Achan, Kannan},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {13515--13526},
 publisher = {Curran Associates, Inc.},
 title = {Adversarial Counterfactual Learning and Evaluation for Recommender System},
 url = {https://proceedings.neurips.cc/paper/2020/file/9cd013fe250ebffc853b386569ab18c0-Paper.pdf},
 volume = {33},
 year = {2020}
}


@inproceedings{Mary18,
 author = {WARLOP, Romain and Lazaric, Alessandro and Mary, J\'{e}r\'{e}mie},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Fighting Boredom in Recommender Systems with Linear Reinforcement Learning},
 url = {https://proceedings.neurips.cc/paper/2018/file/210f760a89db30aa72ca258a3483cc7f-Paper.pdf},
 volume = {31},
 year = {2018}
}

@inproceedings{goodfellow14,
  title={Generative Adversarial Nets},
  author={I. Goodfellow and J. Pouget-Abadie and M. Mirza and B. Xu and D. Warde-Farley and S. Ozair and A. Courville and Y. Bengio},
  booktitle={NIPS},
  year={2014}
}

@article{zhou2020graph,
    author       = {Jie Zhou and
                  Ganqu Cui and
                  Shengding Hu and
                  Zhengyan Zhang and
                  Cheng Yang and
                  Zhiyuan Liu and
                  Lifeng Wang and
                  Changcheng Li and
                  Maosong Sun},
  title        = {Graph neural networks: {A} review of methods and applications},
  journal      = {{AI} Open},
  volume       = {1},
  pages        = {57--81},
  year         = {2020},
}

@inproceedings{KipfW17,
    author    = {T. N. Kipf and M. Welling},
    title     = {Semi-Supervised Classification with Graph Convolutional Networks},
    booktitle = {5th International Conference on Learning Representations, {ICLR} 2017}
}

@inproceedings{William18NIPS,
    author       = {William L. Hamilton and
                  Zhitao Ying and
                  Jure Leskovec},
  editor       = {Isabelle Guyon and
                  Ulrike von Luxburg and
                  Samy Bengio and
                  Hanna M. Wallach and
                  Rob Fergus and
                  S. V. N. Vishwanathan and
                  Roman Garnett},
  title        = {Inductive Representation Learning on Large Graphs},
  year = {2017},
  booktitle    = {Annual Conference
                  on Neural Information Processing Systems 2017},
}



@inproceedings{DBLP:journals/corr/abs-1907-07498,
  author       = {Kalle Hjerppe and
                  Jukka Ruohonen and
                  Ville Lepp{\"{a}}nen},
  editor       = {Daniela E. Damian and
                  Anna Perini and
                  Seok{-}Won Lee},
  title        = {The General Data Protection Regulation: Requirements, Architectures,
                  and Constraints},
  year = {2019},
  booktitle    = {27th {IEEE} International Requirements Engineering Conference, {RE}
                  2019},
}



@inproceedings{DBLP:journals/corr/abs-1810-00826,
  author       = {Xiyuan Wang and
                  Muhan Zhang},
  editor       = {Kamalika Chaudhuri and
                  Stefanie Jegelka and
                  Le Song and
                  Csaba Szepesv{\'{a}}ri and
                  Gang Niu and
                  Sivan Sabato},
  title        = {How Powerful are Spectral Graph Neural Networks},
  year = {2022},
  booktitle    = {International Conference on Machine Learning, {ICML} 2022},
}


@article{kipf2016variational,
  title={Variational Graph Auto-Encoders},
  author={Kipf, Thomas N and Welling, Max},
  journal={NIPS Workshop on Bayesian Deep Learning},
  year={2016}
}

@article{wu2021fedgnn,
  title={Fedgnn: Federated graph neural network for privacy-preserving recommendation},
  author={Wu, Chuhan and Wu, Fangzhao and Cao, Yang and Huang, Yongfeng and Xie, Xing},
  journal={arXiv preprint arXiv:2102.04925},
  year={2021}
}

@inproceedings{kipf2017semisupervised,
      author       = {Thomas N. Kipf and
                  Max Welling},
  title        = {Semi-Supervised Classification with Graph Convolutional Networks},
  year={2017},
  booktitle    = {5th International Conference on Learning Representations, {ICLR} 2017},
}

@misc{wu2021graph,
      title={Graph Neural Networks in Recommender Systems: A Survey}, 
      author={Shiwen Wu and Fei Sun and Wentao Zhang and Bin Cui},
      year={2021},
      eprint={2011.02260},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}

@article{Seldin15,
title = "{PAC}-{B}ayesian {I}nequalities for {M}artingales",
author = {Y. Seldin and F. Laviolette and N. Cesa-Bianchi and J. Shawe-Taylor and P. Auer},
booktitle = {IEEE TRANSACTIONS ON INFORMATION THEORY},
year = {2015}
}

@article{DBLP:journals/corr/KipfW16a,
  author    = {Thomas N. Kipf and
               Max Welling},
  title     = {Variational Graph Auto-Encoders},
  journal   = {CoRR},
  volume    = {abs/1611.07308},
  year      = {2016},
  url       = {http://arxiv.org/abs/1611.07308},
  eprinttype = {arXiv},
  eprint    = {1611.07308},
  timestamp = {Mon, 13 Aug 2018 16:47:18 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/KipfW16a.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2104-04345,
  author    = {Joshua Mitton and
               Hans M. Senn and
               Klaas Wynne and
               Roderick Murray{-}Smith},
  title     = {A Graph {VAE} and Graph Transformer Approach to Generating Molecular
               Graphs},
  journal   = {CoRR},
  volume    = {abs/2104.04345},
  year      = {2021},
  url       = {https://arxiv.org/abs/2104.04345},
  eprinttype = {arXiv},
  eprint    = {2104.04345},
  timestamp = {Tue, 13 Apr 2021 16:46:17 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-04345.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/nips/YingY0RHL18,
  author    = {Zhitao Ying and
               Jiaxuan You and
               Christopher Morris and
               Xiang Ren and
               William L. Hamilton and
               Jure Leskovec},
  editor    = {Samy Bengio and
               Hanna M. Wallach and
               Hugo Larochelle and
               Kristen Grauman and
               Nicol{\`{o}} Cesa{-}Bianchi and
               Roman Garnett},
  title     = {Hierarchical Graph Representation Learning with Differentiable Pooling},
  booktitle = {Advances in Neural Information Processing Systems 31: Annual Conference
               on Neural Information Processing Systems 2018, NeurIPS 2018, December
               3-8, 2018, Montr{\'{e}}al, Canada},
  pages     = {4805--4815},
  year      = {2018},
  url       = {https://proceedings.neurips.cc/paper/2018/hash/e77dbaf6759253c7c6d0efc5690369c7-Abstract.html},
  timestamp = {Mon, 01 Mar 2021 13:46:58 +0100},
  biburl    = {https://dblp.org/rec/conf/nips/YingY0RHL18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@inproceedings{DBLP:conf/nips/YangZSL019,
  author    = {Carl Yang and
               Peiye Zhuang and
               Wenhan Shi and
               Alan Luu and
               Pan Li},
  editor    = {Hanna M. Wallach and
               Hugo Larochelle and
               Alina Beygelzimer and
               Florence d'Alch'e-Buc and
               Emily B. Fox and
               Roman Garnett},
  title     = {Conditional Structure Generation through Graph Variational Generative
               Adversarial Nets},
  booktitle = {Advances in Neural Information Processing Systems 32: Annual Conference
               on Neural Information Processing Systems 2019, NeurIPS 2019, December
               8-14, 2019, Vancouver, BC, Canada},
  pages     = {1338--1349},
  year      = {2019}
}


@inproceedings{DBLP:journals/corr/abs-1902-07153,
  author       = {Felix Wu and
                  Amauri H. Souza Jr. and
                  Tianyi Zhang and
                  Christopher Fifty and
                  Tao Yu and
                  Kilian Q. Weinberger},
  editor       = {Kamalika Chaudhuri and
                  Ruslan Salakhutdinov},
  title        = {Simplifying Graph Convolutional Networks},
  year = {2019},
  booktitle    = {Proceedings of the 36th International Conference on Machine Learning,
                  {ICML} 2019},
}



@inproceedings{DBLP:journals/corr/abs-2006-08848,
  author       = {Canh T. Dinh and
                  Nguyen Hoang Tran and
                  Tuan Dung Nguyen},
  editor       = {Hugo Larochelle and
                  Marc'Aurelio Ranzato and
                  Raia Hadsell and
                  Maria{-}Florina Balcan and
                  Hsuan{-}Tien Lin},
  title        = {Personalized Federated Learning with Moreau Envelopes},
  booktitle    = {Annual Conference
                  on Neural Information Processing Systems 2020, NeurIPS},
  year         = {2020}
}



@inproceedings{DBLP:journals/corr/abs-1908-10400,
  author       = {Alireza Fallah and
                  Aryan Mokhtari and
                  Asuman E. Ozdaglar},
  editor       = {Silvia Chiappa and
                  Roberto Calandra},
  title        = {On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning
                  Algorithms},
  booktitle    = {The 23rd International Conference on Artificial Intelligence and Statistics,
                  {AISTATS}},
  year         = {2020}
}



@inproceedings{zhang2021subgraph,
  author       = {Ke Zhang and
                  Carl Yang and
                  Xiaoxiao Li and
                  Lichao Sun and
                  Siu{-}Ming Yiu},
  editor       = {Marc'Aurelio Ranzato and
                  Alina Beygelzimer and
                  Yann N. Dauphin and
                  Percy Liang and
                  Jennifer Wortman Vaughan},
  title        = {Subgraph Federated Learning with Missing Neighbor Generation},
  year = {2021},
  booktitle    = {Annual Conference
                  on Neural Information Processing Systems 2021, NeurIPS},
}




@article{DBLP:journals/corr/abs-2104-07145,
  author    = {Chaoyang He and
               Keshav Balasubramanian and
               Emir Ceyani and
               Yu Rong and
               Peilin Zhao and
               Junzhou Huang and
               Murali Annavaram and
               Salman Avestimehr},
  title     = {FedGraphNN: {A} Federated Learning System and Benchmark for Graph
               Neural Networks},
  journal   = {CoRR},
  volume    = {abs/2104.07145},
  year      = {2021}
}



@article{DBLP:journals/corr/abs-2009-07351,
  author    = {Meng Jiang and
               Taeho Jung and
               Ryan Karl and
               Tong Zhao},
  title     = {Federated Dynamic {GNN} with Secure Aggregation},
  journal   = {CoRR},
  volume    = {abs/2009.07351},
  year      = {2020}
}




@article{DBLP:journals/corr/abs-2106-02743,
  author    = {Chaoyang He and
               Emir Ceyani and
               Keshav Balasubramanian and
               Murali Annavaram and
               Salman Avestimehr},
  title     = {SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural
               Networks},
  journal   = {CoRR},
  volume    = {abs/2106.02743},
  year      = {2021}
}

@inproceedings{GAT2018,
  author       = {Petar Velickovic and
                  Guillem Cucurull and
                  Arantxa Casanova and
                  Adriana Romero and
                  Pietro Li{\`{o}} and
                  Yoshua Bengio},
  title        = {Graph Attention Networks},
  booktitle    = {6th International Conference on Learning Representations, {ICLR} 2018},
}


@inproceedings{DBLP:journals/corr/abs-2002-09518,
  author       = {Amir Hosein Khas Ahmadi and
                  Kaveh Hassani and
                  Parsa Moradi and
                  Leo Lee and
                  Quaid Morris},
  title        = {Memory-Based Graph Networks},
  booktitle    = {8th International Conference on Learning Representations, {ICLR} 2020},
  publisher    = {OpenReview.net},
  year         = {2020}
}
}




@inproceedings{DBLP:journals/corr/abs-1805-11921,
  author       = {Sergey Ivanov and
                  Evgeny Burnaev},
  editor       = {Jennifer G. Dy and
                  Andreas Krause},
  title        = {Anonymous Walk Embeddings},
  booktitle    = {Proceedings of the 35th International Conference on Machine Learning,
                  {ICML} 2018},
}




@inproceedings{DBLP:journals/corr/abs-2109-05929,
  author       = {Hamed R. Bonab and
                  Mohammad Aliannejadi and
                  Ali Vardasbi and
                  Evangelos Kanoulas and
                  James Allan},
  editor       = {Gianluca Demartini and
                  Guido Zuccon and
                  J. Shane Culpepper and
                  Zi Huang and
                  Hanghang Tong},
  title        = {Cross-Market Product Recommendation},
  booktitle    = {{CIKM} '21: The 30th {ACM} International Conference on Information
                  and Knowledge Management, 2021},
  pages        = {110--119},
  publisher    = {{ACM}},
  year         = {2021}
}


@inproceedings{hoang2022learning,
  title={Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback},
  author={Hoang, Nghia and Deoras, Anoop and Zhao, Tong and Li, Jin and Karypis, George},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={1062--1077},
  year={2022},
  organization={PMLR}
}

@inproceedings{RendleFGS09,
  author    = {Steffen Rendle and
               Christoph Freudenthaler and
               Zeno Gantner and
               Lars Schmidt{-}Thieme},
  editor    = {Jeff A. Bilmes and
               Andrew Y. Ng},
  title     = {{BPR:} Bayesian Personalized Ranking from Implicit Feedback},
  year = {2009},
  booktitle = {Proceedings of the Twenty-Fifth Conference on Uncertainty
               in Artificial Intelligence}
}

@inproceedings{he2020lightgcn,
  title={Lightgcn: Simplifying and powering graph convolution network for recommendation},
  author={He, Xiangnan and Deng, Kuan and Wang, Xiang and Li, Yan and Zhang, Yongdong and Wang, Meng},
  booktitle={Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval},
  pages={639--648},
  year={2020}
}

@inproceedings{wang2019neural,
  title={Neural graph collaborative filtering},
  author={Wang, Xiang and He, Xiangnan and Wang, Meng and Feng, Fuli and Chua, Tat-Seng},
  booktitle={Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval},
  pages={165--174},
  year={2019}
}

@inproceedings{mao2021ultragcn,
  title={UltraGCN: ultra simplification of graph convolutional networks for recommendation},
  author={Mao, Kelong and Zhu, Jieming and Xiao, Xi and Lu, Biao and Wang, Zhaowei and He, Xiuqiang},
  booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
  pages={1253--1262},
  year={2021}
}


@inproceedings{fallah2020personalized,
  author       = {Alireza Fallah and
                  Aryan Mokhtari and
                  Asuman E. Ozdaglar},
  editor       = {Hugo Larochelle and
                  Marc'Aurelio Ranzato and
                  Raia Hadsell and
                  Maria{-}Florina Balcan and
                  Hsuan{-}Tien Lin},
  title        = {Personalized Federated Learning with Theoretical Guarantees: {A} Model-Agnostic
                  Meta-Learning Approach},
  booktitle    = {Annual Conference
                  on Neural Information Processing Systems 2020, NeurIPS},
}

@article{sattler2020clustered,
  title={Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints},
  author={Sattler, Felix and M{\"u}ller, Klaus-Robert and Samek, Wojciech},
  journal={IEEE transactions on neural networks and learning systems},
  volume={32},
  number={8},
  pages={3710--3722},
  year={2020},
  publisher={IEEE}
}

@inproceedings{han2021deeprec,
  title={Deeprec: On-device deep learning for privacy-preserving sequential recommendation in mobile commerce},
  author={Han, Jialiang and Ma, Yun and Mei, Qiaozhu and Liu, Xuanzhe},
  booktitle={Proceedings of the Web Conference 2021},
  pages={900--911},
  year={2021}
}

@inproceedings{lin2020meta,
  title={Meta matrix factorization for federated rating predictions},
  author={Lin, Yujie and Ren, Pengjie and Chen, Zhumin and Ren, Zhaochun and Yu, Dongxiao and Ma, Jun and Rijke, Maarten de and Cheng, Xiuzhen},
  booktitle={Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={981--990},
  year={2020}
}

@misc{kipfwelling16,
  doi = {10.48550/ARXIV.1611.07308},
  url = {https://arxiv.org/abs/1611.07308},
  author = {Kipf, Thomas N. and Welling, Max},
  title = {Variational Graph Auto-Encoders},
  publisher = {arXiv},
  year = {2016},  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@inproceedings{li2021ditto,
  title={Ditto: Fair and robust federated learning through personalization},
  author={Li, Tian and Hu, Shengyuan and Beirami, Ahmad and Smith, Virginia},
  booktitle={International Conference on Machine Learning},
  pages={6357--6368},
  year={2021},
  organization={PMLR}
}

@article{kairouz2021advances,
  title={Advances and open problems in federated learning},
  author={Kairouz, Peter and McMahan, H Brendan and Avent, Brendan and Bellet, Aur{\'e}lien and Bennis, Mehdi and Bhagoji, Arjun Nitin and Bonawitz, Kallista and Charles, Zachary and Cormode, Graham and Cummings, Rachel and others},
  journal={Foundations and Trends{\textregistered} in Machine Learning},
  volume={14},
  number={1--2},
  pages={1--210},
  year={2021},
  publisher={Now Publishers, Inc.}
}

@inproceedings{bonawitz2019federated,
  author       = {Kallista A. Bonawitz and
                  Fariborz Salehi and
                  Jakub Kone{\v{c}}n{\'y} and
                  Brendan McMahan and
                  Marco Gruteser},
  editor       = {Michael B. Matthews},
  title        = {Federated Learning with Autotuned Communication-Efficient Secure Aggregation},
  booktitle    = {53rd Asilomar Conference on Signals, Systems, and Computers, {ACSCC}
                  2019},
  pages        = {1222--1226},
  publisher    = {{IEEE}},
  year         = {2019}
}

@article{liu2022federated,
  title={Federated social recommendation with graph neural network},
  author={Liu, Zhiwei and Yang, Liangwei and Fan, Ziwei and Peng, Hao and Yu, Philip S},
  journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
  volume={13},
  number={4},
  pages={1--24},
  year={2022},
  publisher={ACM New York, NY}
}