@article{einstein,
author =       "Albert Einstein",
title =        "{Zur Elektrodynamik bewegter Körper}",
journal =      "Annalen der Physik",
volume =       "322",
number =       "10",
pages =        "891--921",
year =         "1905"}

@book{latexcompanion,
author    = "Michel Goossens and Frank Mittelbach and Alexander Samarin",
title     = "The \LaTeX\ Companion",
year      = "1993",
publisher = "Addison-Wesley"}

@inproceedings{ghosh2019landmark,
  title={Landmark Ordinal Embedding},
  author={Ghosh, Nikhil and Chen, Yuxin and Yue, Yisong},
  booktitle={Advances in Neural Information Processing Systems},
  pages={11502--11511},
  year={2019}
}


@inproceedings{chen2013near,
  title={Near-optimal batch mode active learning and adaptive submodular optimization},
  author={Chen, Yuxin and Krause, Andreas},
  booktitle={International Conference on Machine Learning},
  pages={160--168},
  year={2013},
  organization={PMLR}
}

@article{kirsch2019batchbald,
  title={Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning},
  author={Kirsch, Andreas and Van Amersfoort, Joost and Gal, Yarin},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@article{actsamlinreg,
  author    = {Cameron Musco and
               Christopher Musco and
               David P. Woodruff and
               Taisuke Yasuda},
  title     = {Active Sampling for Linear Regression Beyond the {\textdollar}{\textbackslash}ell{\_}2{\textdollar}
               Norm},
  journal   = {CoRR},
  volume    = {abs/2111.04888},
  year      = {2021},
  url       = {https://arxiv.org/abs/2111.04888},
  eprinttype = {arXiv},
  eprint    = {2111.04888},
  timestamp = {Wed, 10 Nov 2021 16:07:30 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2111-04888.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  url={https://arxiv.org/abs/2111.04888}
}

@article{learnalgoforactivelearning,
  author    = {Philip Bachman and
               Alessandro Sordoni and
               Adam Trischler},
  title     = {Learning Algorithms for Active Learning},
  journal   = {CoRR},
  volume    = {abs/1708.00088},
  year      = {2017},
  url       = {http://arxiv.org/abs/1708.00088},
  eprinttype = {arXiv},
  eprint    = {1708.00088},
  timestamp = {Mon, 13 Aug 2018 16:47:04 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1708-00088.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{liu-etal-2018-learning-actively,
    title = "Learning How to Actively Learn: A Deep Imitation Learning Approach",
    author = "Liu, Ming  and
      Buntine, Wray  and
      Haffari, Gholamreza",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1174",
    doi = "10.18653/v1/P18-1174",
    pages = "1874--1883",
    abstract = "Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary. We introduce a method that learns an AL {``}policy{''} using {``}imitation learning{''} (IL). Our IL-based approach makes use of an efficient and effective {``}algorithmic expert{''}, which provides the policy learner with good actions in the encountered AL situations. The AL strategy is then learned with a feedforward network, mapping situations to most informative query datapoints. We evaluate our method on two different tasks: text classification and named entity recognition. Experimental results show that our IL-based AL strategy is more effective than strong previous methods using heuristics and reinforcement learning.",
}
@article{learntoactivelearning,
  author    = {Meng Fang and
               Yuan Li and
               Trevor Cohn},
  title     = {Learning how to Active Learn: {A} Deep Reinforcement Learning Approach},
  journal   = {CoRR},
  volume    = {abs/1708.02383},
  year      = {2017},
  url       = {http://arxiv.org/abs/1708.02383},
  eprinttype = {arXiv},
  eprint    = {1708.02383},
  timestamp = {Fri, 29 Nov 2019 08:56:41 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1708-02383.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{metalearnbandits,
  author    = {Branislav Kveton and
               Martin Mladenov and
               Chih{-}Wei Hsu and
               Manzil Zaheer and
               Csaba Szepesv{\'{a}}ri and
               Craig Boutilier},
  title     = {Differentiable Meta-Learning in Contextual Bandits},
  journal   = {CoRR},
  volume    = {abs/2006.05094},
  year      = {2020},
  url       = {https://arxiv.org/abs/2006.05094},
  eprinttype = {arXiv},
  eprint    = {2006.05094},
  timestamp = {Fri, 12 Jun 2020 14:02:57 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2006-05094.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{intsubmodsetcov,
  title={Interactive submodular set cover},
  author={Guillory, Andrew and Bilmes, Jeff},
  journal={arXiv preprint arXiv:1002.3345},
  year={2010}
}
@article{golovinkrause,
  author    = {Daniel Golovin and
               Andreas Krause},
  title     = {Adaptive Submodularity: {A} New Approach to Active Learning and Stochastic
               Optimization},
  journal   = {CoRR},
  volume    = {abs/1003.3967},
  year      = {2010},
  url       = {http://arxiv.org/abs/1003.3967},
  eprinttype = {arXiv},
  eprint    = {1003.3967},
  timestamp = {Mon, 13 Aug 2018 16:47:14 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1003-3967.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{lewis1994,
  abstract = {The ability to cheaply train text classifiers is critical to their use in
information retrieval, content analysis, natural language processing, and other
tasks involving data which is partly or fully textual. An algorithm for
sequential sampling during machine learning of statistical classifiers was
developed and tested on a newswire text categorization task. This method, which
we call uncertainty sampling, reduced by as much as 500-fold the amount of
training data that would have to be manually classified to achieve a given
level of effectiveness.},
  added-at = {2018-12-08T22:13:50.000+0100},
  author = {Lewis, David D. and Gale, William A.},
  biburl = {https://www.bibsonomy.org/bibtex/2d73c107e97fe23a6fb162985817d7201/lopusz_kdd},
  description = {[cmp-lg/9407020] A Sequential Algorithm for Training Text Classifiers},
  interhash = {79828b845692edcf1d41b8f9266a924d},
  intrahash = {d73c107e97fe23a6fb162985817d7201},
  keywords = {active_learning general_text_mining},
  note = {active learning roots},
  timestamp = {2018-12-08T22:13:50.000+0100},
  title = {A Sequential Algorithm for Training Text Classifiers},
  url = {http://arxiv.org/abs/cmp-lg/9407020},
  year = 1994
}


@article{derezinski2018leveraged,
  title={Leveraged volume sampling for linear regression},
  author={Derezinski, Michal and Warmuth, Manfred KK and Hsu, Daniel J},
  journal={Advances in Neural Information Processing Systems},
  volume={31},
  year={2018}
}

@article{PupoV18,
  title = {Evolutionary Strategy to Perform Batch-Mode Active Learning on Multi-Label Data},
  author = {Oscar Gabriel Reyes Pupo and Sebastián Ventura},
  year = {2018},
  doi = {10.1145/3161606},
  url = {http://doi.acm.org/10.1145/3161606},
  researchr = {https://researchr.org/publication/PupoV18},
  cites = {0},
  citedby = {0},
  journal = {ACM TIST},
  volume = {9},
  number = {4},
}
@article{oneroundactivelearn,
  author    = {Tianhao Wang and
               Si Chen and
               Ruoxi Jia},
  title     = {One-Round Active Learning},
  journal   = {CoRR},
  volume    = {abs/2104.11843},
  year      = {2021},
  url       = {https://arxiv.org/abs/2104.11843},
  eprinttype = {arXiv},
  eprint    = {2104.11843},
  timestamp = {Mon, 03 May 2021 17:38:30 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-11843.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{optimalsamplinggap,
  author    = {Shaojie Tang and
               Jing Yuan},
  title     = {Optimal Sampling Gaps for Adaptive Submodular Maximization},
  journal   = {CoRR},
  volume    = {abs/2104.01750},
  year      = {2021},
  url       = {https://arxiv.org/abs/2104.01750},
  eprinttype = {arXiv},
  eprint    = {2104.01750},
  timestamp = {Mon, 12 Apr 2021 16:14:56 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-01750.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{glister,
  author    = {KrishnaTeja Killamsetty and
               Durga Sivasubramanian and
               Ganesh Ramakrishnan and
               Rishabh K. Iyer},
  title     = {{GLISTER:} Generalization based Data Subset Selection for Efficient
               and Robust Learning},
  journal   = {CoRR},
  volume    = {abs/2012.10630},
  year      = {2020},
  url       = {https://arxiv.org/abs/2012.10630},
  eprinttype = {arXiv},
  eprint    = {2012.10630},
  timestamp = {Mon, 04 Jan 2021 16:33:46 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2012-10630.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{pmlr-v37-wei15,
  title = 	 {Submodularity in Data Subset Selection and Active Learning},
  author = 	 {Wei, Kai and Iyer, Rishabh and Bilmes, Jeff},
  booktitle = 	 {Proceedings of the 32nd International Conference on Machine Learning},
  pages = 	 {1954--1963},
  year = 	 {2015},
  editor = 	 {Bach, Francis and Blei, David},
  volume = 	 {37},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Lille, France},
  month = 	 {07--09 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v37/wei15.pdf},
  url = 	 {https://proceedings.mlr.press/v37/wei15.html},
  abstract = 	 {We study the problem of selecting a subset of big data to train a classifier while incurring minimal performance loss. We show the connection of submodularity to the data likelihood functions for Naive Bayes (NB) and Nearest Neighbor (NN) classifiers, and formulate the data subset selection problems for these classifiers as constrained submodular maximization. Furthermore, we apply this framework to active learning and propose a novel scheme filtering active submodular selection (FASS), where we combine the uncertainty sampling method with a submodular data subset selection framework. We extensively evaluate the proposed framework on text categorization and handwritten digit recognition tasks with four different classifiers, including Deep Neural Network (DNN) based classifiers. Empirical results indicate that the proposed framework yields significant improvement over the state-of-the-art algorithms on all classifiers.}
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% Domain Adaptation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@inproceedings{ganin2015unsupervised,
  title={Unsupervised domain adaptation by backpropagation},
  author={Ganin, Yaroslav and Lempitsky, Victor},
  booktitle={International conference on machine learning},
  pages={1180--1189},
  year={2015},
  organization={PMLR}
}

@inproceedings{hoffman2018cycada,
  title={Cycada: Cycle-consistent adversarial domain adaptation},
  author={Hoffman, Judy and Tzeng, Eric and Park, Taesung and Zhu, Jun-Yan and Isola, Phillip and Saenko, Kate and Efros, Alexei and Darrell, Trevor},
  booktitle={International conference on machine learning},
  pages={1989--1998},
  year={2018},
  organization={PMLR}
}

@inproceedings{rai2010domain,
  title={Domain adaptation meets active learning},
  author={Rai, Piyush and Saha, Avishek and Daum{\'e} III, Hal and Venkatasubramanian, Suresh},
  booktitle={Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing},
  pages={27--32},
  year={2010}
}

@inproceedings{su2020active,
  title={Active adversarial domain adaptation},
  author={Su, Jong-Chyi and Tsai, Yi-Hsuan and Sohn, Kihyuk and Liu, Buyu and Maji, Subhransu and Chandraker, Manmohan},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={739--748},
  year={2020}
}

@inproceedings{saenko2010adapting,
  title={Adapting visual category models to new domains},
  author={Saenko, Kate and Kulis, Brian and Fritz, Mario and Darrell, Trevor},
  booktitle={European conference on computer vision},
  pages={213--226},
  year={2010},
  organization={Springer}
}

@inproceedings{prabhu2021active,
  title={Active domain adaptation via clustering uncertainty-weighted embeddings},
  author={Prabhu, Viraj and Chandrasekaran, Arjun and Saenko, Kate and Hoffman, Judy},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={8505--8514},
  year={2021}
}


@inproceedings{rangwani2021s3vaada,
  title={S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation},
  author={Rangwani, Harsh and Jain, Arihant and Aithal, Sumukh K and Babu, R Venkatesh},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={7516--7525},
  year={2021}
}

%%%%%%%%%%%%%%%%%%%%%% 
%%%%Sample - efficient RL
%%%%%%%%%%%%%%%%%%%%%%


@article{ibarz2021train,
  title={How to train your robot with deep reinforcement learning: lessons we have learned},
  author={Ibarz, Julian and Tan, Jie and Finn, Chelsea and Kalakrishnan, Mrinal and Pastor, Peter and Levine, Sergey},
  journal={The International Journal of Robotics Research},
  volume={40},
  number={4-5},
  pages={698--721},
  year={2021},
  publisher={SAGE Publications Sage UK: London, England}
}

@article{degrave2022magnetic,
  title={Magnetic control of tokamak plasmas through deep reinforcement learning},
  author={Degrave, Jonas and Felici, Federico and Buchli, Jonas and Neunert, Michael and Tracey, Brendan and Carpanese, Francesco and Ewalds, Timo and Hafner, Roland and Abdolmaleki, Abbas and de Las Casas, Diego and others},
  journal={Nature},
  volume={602},
  number={7897},
  pages={414--419},
  year={2022},
  publisher={Nature Publishing Group}
}


@article{mnih2013playing,
  title={Playing atari with deep reinforcement learning},
  author={Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Graves, Alex and Antonoglou, Ioannis and Wierstra, Daan and Riedmiller, Martin},
  journal={arXiv preprint arXiv:1312.5602},
  year={2013}
}

@inproceedings{jin2020provably,
  title={Provably efficient reinforcement learning with linear function approximation},
  author={Jin, Chi and Yang, Zhuoran and Wang, Zhaoran and Jordan, Michael I},
  booktitle={Conference on Learning Theory},
  pages={2137--2143},
  year={2020},
  organization={PMLR},
  url={https://arxiv.org/abs/1907.05388}
}

@article{wagenmaker2021first,
  title={First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach},
  author={Wagenmaker, Andrew and Chen, Yifang and Simchowitz, Max and Du, Simon S and Jamieson, Kevin},
  journal={arXiv preprint arXiv:2112.03432},
  year={2021}
}
%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%% Linear MDP
%%%%%%%%%%%%%%%%%%%%%%%%%%
@article{bradtke1996linear,
  title={Linear least-squares algorithms for temporal difference learning},
  author={Bradtke, Steven J and Barto, Andrew G},
  journal={Machine learning},
  volume={22},
  number={1},
  pages={33--57},
  year={1996},
  publisher={Springer}
}

@inproceedings{melo2007q,
  title={Q-learning with linear function approximation},
  author={Melo, Francisco S and Ribeiro, M Isabel},
  booktitle={International Conference on Computational Learning Theory},
  pages={308--322},
  year={2007},
  organization={Springer}
}

@article{chen2022active,
  title={Active Multi-Task Representation Learning},
  author={Chen, Yifang and Du, Simon S and Jamieson, Kevin},
  journal={arXiv preprint arXiv:2202.00911},
  year={2022},
  url={https://arxiv.org/abs/2202.00911}
}


@inproceedings{NEURIPS2018_e5841df2,
 author = {Shim, Hajin and Hwang, Sung Ju and Yang, Eunho},
 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 = {Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding},
 url = {https://proceedings.neurips.cc/paper/2018/file/e5841df2166dd424a57127423d276bbe-Paper.pdf},
 volume = {31},
 year = {2018}
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%% Linear Mixture
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

@inproceedings{jia2020model,
  title={Model-based reinforcement learning with value-targeted regression},
  author={Jia, Zeyu and Yang, Lin and Szepesvari, Csaba and Wang, Mengdi},
  booktitle={Learning for Dynamics and Control},
  pages={666--686},
  year={2020},
  organization={PMLR}
}

@inproceedings{zhou2021nearly,
  title={Nearly minimax optimal reinforcement learning for linear mixture markov decision processes},
  author={Zhou, Dongruo and Gu, Quanquan and Szepesvari, Csaba},
  booktitle={Conference on Learning Theory},
  pages={4532--4576},
  year={2021},
  organization={PMLR}
}



%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Budget Aware Few round AL
%%%%%%%%%%%%%%%%%%%%%%%%%%

@article{yan2022budget,
  title={Budget-aware Few-shot Learning via Graph Convolutional Network},
  author={Yan, Shipeng and Zhang, Songyang and He, Xuming},
  journal={arXiv preprint arXiv:2201.02304},
  year={2022}
}

@inproceedings{kang2019few,
  title={Few-shot object detection via feature reweighting},
  author={Kang, Bingyi and Liu, Zhuang and Wang, Xin and Yu, Fisher and Feng, Jiashi and Darrell, Trevor},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={8420--8429},
  year={2019}
}

@inproceedings{hu2019attention,
  title={Attention-based multi-context guiding for few-shot semantic segmentation},
  author={Hu, Tao and Yang, Pengwan and Zhang, Chiliang and Yu, Gang and Mu, Yadong and Snoek, Cees GM},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={33},
  number={01},
  pages={8441--8448},
  year={2019}
}

%%%%%% Planning AL heuristics %%%%%%%%%%
@inproceedings{yoo2019learning,
  title={Learning loss for active learning},
  author={Yoo, Donggeun and Kweon, In So},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={93--102},
  year={2019}
}

@article{shannon2001mathematical,
  title={A mathematical theory of communication},
  author={Shannon, Claude Elwood},
  journal={ACM SIGMOBILE mobile computing and communications review},
  volume={5},
  number={1},
  pages={3--55},
  year={2001},
  publisher={ACM New York, NY, USA}
}


%%%%% uncertainty 
@article{settles2012active,
  title={Active learning},
  author={Settles, Burr},
  journal={Synthesis lectures on artificial intelligence and machine learning},
  volume={6},
  number={1},
  pages={1--114},
  year={2012},
  publisher={Morgan \& Claypool Publishers}
}

@article{shen2017deep,
  title={Deep active learning for named entity recognition},
  author={Shen, Yanyao and Yun, Hyokun and Lipton, Zachary C and Kronrod, Yakov and Anandkumar, Animashree},
  journal={arXiv preprint arXiv:1707.05928},
  year={2017}
}


@inproceedings{gal2017deep,
  title={Deep bayesian active learning with image data},
  author={Gal, Yarin and Islam, Riashat and Ghahramani, Zoubin},
  booktitle={International Conference on Machine Learning},
  pages={1183--1192},
  year={2017},
  organization={PMLR}
}
%%%%%


%%%%% greedy k center %%%%
@article{sener2017active,
  title={Active learning for convolutional neural networks: A core-set approach},
  author={Sener, Ozan and Savarese, Silvio},
  journal={arXiv preprint arXiv:1708.00489},
  year={2017}
}



%%%%%%%%% Learning based AL %%%%%%%%%%%
@inproceedings{sinha2019variational,
  title={Variational adversarial active learning},
  author={Sinha, Samarth and Ebrahimi, Sayna and Darrell, Trevor},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={5972--5981},
  year={2019}
}


%%%%%%%% Submodular
@inproceedings{agarwal2019stochastic,
  title={Stochastic submodular cover with limited adaptivity},
  author={Agarwal, Arpit and Assadi, Sepehr and Khanna, Sanjeev},
  booktitle={Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms},
  pages={323--342},
  year={2019},
  organization={SIAM}
}

@book{fujishige2005submodular,
  title={Submodular functions and optimization},
  author={Fujishige, Satoru},
  year={2005},
  publisher={Elsevier}
}

@article{nemhauser1978analysis,
  title={An analysis of approximations for maximizing submodular set functions—I},
  author={Nemhauser, George L and Wolsey, Laurence A and Fisher, Marshall L},
  journal={Mathematical programming},
  volume={14},
  number={1},
  pages={265--294},
  year={1978},
  publisher={Springer}
}

@inproceedings{zhang2022stochastic,
  title={Stochastic continuous submodular maximization: Boosting via non-oblivious function},
  author={Zhang, Qixin and Deng, Zengde and Chen, Zaiyi and Hu, Haoyuan and Yang, Yu},
  booktitle={International Conference on Machine Learning},
  pages={26116--26134},
  year={2022},
  organization={PMLR}
}

@article{zhou2022distributed,
  title={Distributed Attack-Robust Submodular Maximization for Multirobot Planning},
  author={Zhou, Lifeng and Tzoumas, Vasileios and Pappas, George J and Tokekar, Pratap},
  journal={IEEE Transactions on Robotics},
  year={2022},
  publisher={IEEE}
}

@inproceedings{alieva2020learning,
  title={Learning to make decisions via submodular regularization},
  author={Alieva, Ayya and Aceves, Aiden and Song, Jialin and Mayo, Stephen and Yue, Yisong and Chen, Yuxin},
  booktitle={International Conference on Learning Representations},
  year={2020}
}


@article{guillory2010interactive,
  title={Interactive submodular set cover},
  author={Guillory, Andrew and Bilmes, Jeff},
  journal={arXiv preprint arXiv:1002.3345},
  year={2010}
}

@inproceedings{tschiatschek2017selecting,
  title={Selecting sequences of items via submodular maximization},
  author={Tschiatschek, Sebastian and Singla, Adish and Krause, Andreas},
  booktitle={Thirty-First AAAI Conference on Artificial Intelligence},
  year={2017}
}

@inproceedings{guillory2011simultaneous,
  title={Simultaneous learning and covering with adversarial noise},
  author={Guillory, Andrew and Bilmes, Jeff A},
  booktitle={ICML},
  year={2011}
}


%%%%%%%%%% generalization error %%%%%%%%%%
@article{russo2019much,
  title={How much does your data exploration overfit? controlling bias via information usage},
  author={Russo, Daniel and Zou, James},
  journal={IEEE Transactions on Information Theory},
  volume={66},
  number={1},
  pages={302--323},
  year={2019},
  publisher={IEEE}
}


%%%%%%%%% adaptive submolarity
@inproceedings{golovin2010adaptive,
  title={Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization.},
  author={Golovin, Daniel and Krause, Andreas},
  booktitle={COLT},
  pages={333--345},
  year={2010},
  organization={Citeseer}
}

@article{xu2017information,
  title={Information-theoretic analysis of generalization capability of learning algorithms},
  author={Xu, Aolin and Raginsky, Maxim},
  journal={Advances in Neural Information Processing Systems},
  volume={30},
  year={2017}
}

%%%%%%%%% Mcdiarmid %%%%%%%%%
@incollection{mcdiarmid1998concentration,
  title={Concentration},
  author={McDiarmid, Colin},
  booktitle={Probabilistic methods for algorithmic discrete mathematics},
  pages={195-248},
  year={1998},
  publisher={Springer}
}

@article{talagrand1995concentration,
  title={Concentration of measure and isoperimetric inequalities in product spaces},
  author={Talagrand, Michel},
  journal={Publications Math{\'e}matiques de l'Institut des Hautes Etudes Scientifiques},
  volume={81},
  number={1},
  pages={73-205},
  year={1995},
  publisher={Springer}
}

@book{boucheron2013concentration,
  title={Concentration inequalities: A nonasymptotic theory of independence},
  author={Boucheron, St{\'e}phane and Lugosi, G{\'a}bor and Massart, Pascal},
  year={2013},
  publisher={Oxford university press}
}


@inproceedings{chen2022near,
  title={Near-Optimal Task Selection for Meta-Learning with Mutual Information and Online Variational Bayesian Unlearning},
  author={Chen, Yizhou and Zhang, Shizhuo and Low, Bryan Kian Hsiang},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={9091--9113},
  year={2022},
  organization={PMLR}
}

@article{rivasplata2020pac,
  title={PAC-Bayes analysis beyond the usual bounds},
  author={Rivasplata, Omar and Kuzborskij, Ilja and Szepesv{\'a}ri, Csaba and Shawe-Taylor, John},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={16833--16845},
  year={2020}
}

@inproceedings{chen2019active,
  title={Active regression via linear-sample sparsification},
  author={Chen, Xue and Price, Eric},
  booktitle={Conference on Learning Theory},
  pages={663--695},
  year={2019},
  organization={PMLR}
}

@article{sviridenko2017optimal,
  title={Optimal approximation for submodular and supermodular optimization with bounded curvature},
  author={Sviridenko, Maxim and Vondr{\'a}k, Jan and Ward, Justin},
  journal={Mathematics of Operations Research},
  volume={42},
  number={4},
  pages={1197--1218},
  year={2017},
  publisher={INFORMS}
}

@article{chekuri2009randomized,
  title={Randomized pipage rounding for matroid polytopes and applications},
  author={Chekuri, Chandra and Vondr{\'a}k, Jan},
  journal={CoRR, abs/0909.4348},
  volume={4},
  year={2009},
  publisher={Citeseer}
}

@inproceedings{goemans2009approximating,
  title={Approximating submodular functions everywhere},
  author={Goemans, Michel X and Harvey, Nicholas JA and Iwata, Satoru and Mirrokni, Vahab},
  booktitle={Proceedings of the twentieth annual ACM-SIAM symposium on Discrete algorithms},
  pages={535--544},
  year={2009},
  organization={SIAM}
}

@inproceedings{balcan2011learning,
  title={Learning submodular functions},
  author={Balcan, Maria-Florina and Harvey, Nicholas JA},
  booktitle={Proceedings of the forty-third annual ACM symposium on Theory of computing},
  pages={793--802},
  year={2011}
}

@inproceedings{zhang2005data,
  title={Data dependent concentration bounds for sequential prediction algorithms},
  author={Zhang, Tong},
  booktitle={International Conference on Computational Learning Theory},
  pages={173--187},
  year={2005},
  organization={Springer}
}

@inproceedings{gupta2016algorithms,
  title={Algorithms and adaptivity gaps for stochastic probing},
  author={Gupta, Anupam and Nagarajan, Viswanath and Singla, Sahil},
  booktitle={Proceedings of the twenty-seventh annual ACM-SIAM symposium on Discrete algorithms},
  pages={1731--1747},
  year={2016},
  organization={SIAM}
}

@article{vondrak2010note,
  title={A note on concentration of submodular functions},
  author={Vondr{\'a}k, Jan},
  journal={arXiv preprint arXiv:1005.2791},
  year={2010}
}

@article{zhang2015string,
  title={String submodular functions with curvature constraints},
  author={Zhang, Zhenliang and Chong, Edwin KP and Pezeshki, Ali and Moran, William},
  journal={IEEE Transactions on Automatic Control},
  volume={61},
  number={3},
  pages={601--616},
  year={2015},
  publisher={IEEE}
}

@article{sallam2020robust,
  title={Robust sequence submodular maximization},
  author={Sallam, Gamal and Zheng, Zizhan and Wu, Jie and Ji, Bo},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={15417--15426},
  year={2020}
}

@article{alaei2021maximizing,
  title={Maximizing sequence-submodular functions and its application to online advertising},
  author={Alaei, Saeed and Makhdoumi, Ali and Malekian, Azarakhsh},
  journal={Management Science},
  volume={67},
  number={10},
  pages={6030--6054},
  year={2021},
  publisher={INFORMS}
}

@article{streeter2008online,
  title={An online algorithm for maximizing submodular functions},
  author={Streeter, Matthew and Golovin, Daniel},
  journal={Advances in Neural Information Processing Systems},
  volume={21},
  year={2008}
}

@article{derezinski2019unbiased,
  title={Unbiased estimators for random design regression},
  author={Derezi{\'n}ski, Micha{\l} and Warmuth, Manfred K and Hsu, Daniel},
  journal={arXiv preprint arXiv:1907.03411},
  year={2019}
}



@article{vershynin2010introduction,
  title={Introduction to the non-asymptotic analysis of random matrices},
  author={Vershynin, Roman},
  journal={arXiv preprint arXiv:1011.3027},
  year={2010}
}

@article{rosenfeld2019constructive,
  title={A constructive prediction of the generalization error across scales},
  author={Rosenfeld, Jonathan S and Rosenfeld, Amir and Belinkov, Yonatan and Shavit, Nir},
  journal={arXiv preprint arXiv:1909.12673},
  year={2019}
}

@article{hestness2017deep,
  title={Deep learning scaling is predictable, empirically},
  author={Hestness, Joel and Narang, Sharan and Ardalani, Newsha and Diamos, Gregory and Jun, Heewoo and Kianinejad, Hassan and Patwary, Md and Ali, Mostofa and Yang, Yang and Zhou, Yanqi},
  journal={arXiv preprint arXiv:1712.00409},
  year={2017}
}

@inproceedings{hashimoto2021model,
  title={Model Performance Scaling with Multiple Data Sources},
  author={Hashimoto, Tatsunori},
  booktitle={International Conference on Machine Learning},
  pages={4107--4116},
  year={2021},
  organization={PMLR}
}


@article{kulesza2012determinantal,
  title={Determinantal point processes for machine learning},
  author={Kulesza, Alex and Taskar, Ben and others},
  journal={Foundations and Trends{\textregistered} in Machine Learning},
  volume={5},
  number={2--3},
  pages={123--286},
  year={2012},
  publisher={Now Publishers, Inc.}
}

@inproceedings{kulesza2011k,
  title={k-DPPs: Fixed-size determinantal point processes},
  author={Kulesza, Alex and Taskar, Ben},
  booktitle={Proceedings of the 28th International Conference on Machine Learning (ICML-11)},
  pages={1193--1200},
  year={2011}
}


@inproceedings{mothilal2020explaining,
  title={Explaining machine learning classifiers through diverse counterfactual explanations},
  author={Mothilal, Ramaravind K and Sharma, Amit and Tan, Chenhao},
  booktitle={Proceedings of the 2020 conference on fairness, accountability, and transparency},
  pages={607--617},
  year={2020}
}

@article{lavancier2015determinantal,
  title={Determinantal point process models and statistical inference},
  author={Lavancier, Fr{\'e}d{\'e}ric and M{\o}ller, Jesper and Rubak, Ege},
  journal={Journal of the Royal Statistical Society: Series B: Statistical Methodology},
  pages={853--877},
  year={2015},
  publisher={JSTOR}
}

@article{borodin2005eynard,
  title={Eynard--Mehta theorem, Schur process, and their Pfaffian analogs},
  author={Borodin, Alexei and Rains, Eric M},
  journal={Journal of statistical physics},
  volume={121},
  pages={291--317},
  year={2005},
  publisher={Springer}
}


@article{hochbaum1998analysis,
  title={Analysis of the greedy approach in problems of maximum k-coverage},
  author={Hochbaum, Dorit S and Pathria, Anu},
  journal={Naval Research Logistics (NRL)},
  volume={45},
  number={6},
  pages={615--627},
  year={1998},
  publisher={Wiley Online Library}
}

@article{horel2016maximization,
  title={Maximization of approximately submodular functions},
  author={Horel, Thibaut and Singer, Yaron},
  journal={Advances in neural information processing systems},
  volume={29},
  year={2016}
}

@inproceedings{mirzasoleiman2015lazier,
  title={Lazier than lazy greedy},
  author={Mirzasoleiman, Baharan and Badanidiyuru, Ashwinkumar and Karbasi, Amin and Vondr{\'a}k, Jan and Krause, Andreas},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={29},
  number={1},
  year={2015}
}


@inproceedings{cormode2010set,
  title={Set cover algorithms for very large datasets},
  author={Cormode, Graham and Karloff, Howard and Wirth, Anthony},
  booktitle={Proceedings of the 19th ACM international conference on Information and knowledge management},
  pages={479--488},
  year={2010}
}

 @misc{goethals, title={Frequent itemset mining dataset repository.}, url={http://fimi.uantwerpen.be/fimi04/}, journal={Workshop on frequent itemset mining implementations (FIMI'04)}, author={Goethals, Bart}} 

%%%% active learning version space reduction
 @article{mitchell1982generalization,
  title={Generalization as search},
  author={Mitchell, Tom M},
  journal={Artificial intelligence},
  volume={18},
  number={2},
  pages={203--226},
  year={1982},
  publisher={Elsevier}
}

@inproceedings{he2004mean,
  title={Mean version space: a new active learning method for content-based image retrieval},
  author={He, Jingrui and Tong, Hanghang and Li, Mingjing and Zhang, Hong-Jiang and Zhang, Changshui},
  booktitle={Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval},
  pages={15--22},
  year={2004}
}

@article{roy2016active,
  title={Active learning with version spaces for object detection},
  author={Roy, Soumya and Namboodiri, Vinay P and Biswas, Arijit},
  journal={arXiv preprint arXiv:1611.07285},
  year={2016}
}

@inproceedings{indyk2014composable,
  title={Composable core-sets for diversity and coverage maximization},
  author={Indyk, Piotr and Mahabadi, Sepideh and Mahdian, Mohammad and Mirrokni, Vahab S},
  booktitle={Proceedings of the 33rd ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems},
  pages={100--108},
  year={2014}
}

@article{iyer2013submodular,
  title={Submodular optimization with submodular cover and submodular knapsack constraints},
  author={Iyer, Rishabh K and Bilmes, Jeff A},
  journal={Advances in neural information processing systems},
  volume={26},
  year={2013}
}

@article{morris2020predicting,
  title={Predicting binding from screening assays with transformer network embeddings},
  author={Morris, Paul and St. Clair, Rachel and Hahn, William Edward and Barenholtz, Elan},
  journal={Journal of Chemical Information and Modeling},
  volume={60},
  number={9},
  pages={4191--4199},
  year={2020},
  publisher={ACS Publications}
}

@inproceedings{ribeiro2020sketchformer,
  title={Sketchformer: Transformer-based representation for sketched structure},
  author={Ribeiro, Leo Sampaio Ferraz and Bui, Tu and Collomosse, John and Ponti, Moacir},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={14153--14162},
  year={2020}
}



@inproceedings{
mahmood2022lowbudget,
title={Low-Budget Active Learning via Wasserstein Distance: An Integer Programming Approach},
author={Rafid Mahmood and Sanja Fidler and Marc T Law},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=v8OlxjGn23S}
}

@article{cuturi2013sinkhorn,
  title={Sinkhorn distances: Lightspeed computation of optimal transport},
  author={Cuturi, Marco},
  journal={Advances in neural information processing systems},
  volume={26},
  year={2013}
}


@article{alvarez2020geometric,
  title={Geometric dataset distances via optimal transport},
  author={Alvarez-Melis, David and Fusi, Nicolo},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={21428--21439},
  year={2020}
}

@inproceedings{alvarez2021dataset,
  title={Dataset dynamics via gradient flows in probability space},
  author={Alvarez-Melis, David and Fusi, Nicol{\`o}},
  booktitle={International Conference on Machine Learning},
  pages={219--230},
  year={2021},
  organization={PMLR}
}


@article{yehuda2022active,
  title={Active learning through a covering lens},
  author={Yehuda, Ofer and Dekel, Avihu and Hacohen, Guy and Weinshall, Daphna},
  journal={arXiv preprint arXiv:2205.11320},
  year={2022}
}


%%% BALD
@article{houlsby2011bayesian,
  title={Bayesian active learning for classification and preference learning},
  author={Houlsby, Neil and Husz{\'a}r, Ferenc and Ghahramani, Zoubin and Lengyel, M{\'a}t{\'e}},
  journal={arXiv preprint arXiv:1112.5745},
  year={2011}
}

%% surrogate model
@article{kossen2022active,
  title={Active surrogate estimators: An active learning approach to label-efficient model evaluation},
  author={Kossen, Jannik and Farquhar, Sebastian and Gal, Yarin and Rainforth, Thomas},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={24557--24570},
  year={2022}
}

@article{kim2021lada,
  title={Lada: Look-ahead data acquisition via augmentation for deep active learning},
  author={Kim, Yoon-Yeong and Song, Kyungwoo and Jang, JoonHo and Moon, Il-Chul},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={22919--22930},
  year={2021}
}


@inproceedings{ilyas2022datamodels,
  title={Datamodels: Understanding predictions with data and data with predictions},
  author={Ilyas, Andrew and Park, Sung Min and Engstrom, Logan and Leclerc, Guillaume and Madry, Aleksander},
  booktitle={International Conference on Machine Learning},
  pages={9525--9587},
  year={2022},
  organization={PMLR}
}

%% uncertainty
@inproceedings{tamkin2022active,
 author = {Tamkin, Alex and Nguyen, Dat and Deshpande, Salil and Mu, Jesse and Goodman, Noah},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
 pages = {28140--28153},
 publisher = {Curran Associates, Inc.},
 title = {Active Learning Helps Pretrained Models Learn the Intended Task},
 url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/b43a0e8a35b1c044b18cd843b9771915-Paper-Conference.pdf},
 volume = {35},
 year = {2022}
}

@book{o2014analysis,
  title={Analysis of boolean functions},
  author={O'Donnell, Ryan},
  year={2014},
  publisher={Cambridge University Press}
}

@article{park2022meta,
  title={Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning},
  author={Park, Dongmin and Shin, Yooju and Bang, Jihwan and Lee, Youngjun and Song, Hwanjun and Lee, Jae-Gil},
  journal={arXiv preprint arXiv:2210.07805},
  year={2022}
}


@inproceedings{xie2022towards,
  title={Towards fewer annotations: Active learning via region impurity and prediction uncertainty for domain adaptive semantic segmentation},
  author={Xie, Binhui and Yuan, Longhui and Li, Shuang and Liu, Chi Harold and Cheng, Xinjing},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8068--8078},
  year={2022}
}

@article{konyushkova2017learning,
  title={Learning active learning from data},
  author={Konyushkova, Ksenia and Sznitman, Raphael and Fua, Pascal},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@inproceedings{li2021active,
  title={Active feature acquisition with generative surrogate models},
  author={Li, Yang and Oliva, Junier},
  booktitle={International Conference on Machine Learning},
  pages={6450--6459},
  year={2021},
  organization={PMLR}
}

@article{coleman2019selection,
  title={Selection via proxy: Efficient data selection for deep learning},
  author={Coleman, Cody and Yeh, Christopher and Mussmann, Stephen and Mirzasoleiman, Baharan and Bailis, Peter and Liang, Percy and Leskovec, Jure and Zaharia, Matei},
  journal={arXiv preprint arXiv:1906.11829},
  year={2019}
}

@article{citovsky2021batch,
  title={Batch active learning at scale},
  author={Citovsky, Gui and DeSalvo, Giulia and Gentile, Claudio and Karydas, Lazaros and Rajagopalan, Anand and Rostamizadeh, Afshin and Kumar, Sanjiv},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={11933--11944},
  year={2021}
}

@article{ash2019deep,
  title={Deep batch active learning by diverse, uncertain gradient lower bounds},
  author={Ash, Jordan T and Zhang, Chicheng and Krishnamurthy, Akshay and Langford, John and Agarwal, Alekh},
  journal={arXiv preprint arXiv:1906.03671},
  year={2019}
}

@article{saunshi2022understanding,
  title={Understanding Influence Functions and Datamodels via Harmonic Analysis},
  author={Saunshi, Nikunj and Gupta, Arushi and Braverman, Mark and Arora, Sanjeev},
  journal={arXiv preprint arXiv:2210.01072},
  year={2022}
}


%% DeepSets
@inproceedings{
zeng2023modelpred,
title={ModelPred: A Framework for Predicting Trained Model from Training Data},
author={Yingyan Zeng and Tianhao Wang and Si Chen and Hoang Anh Just and Ran Jin and Ruoxi Jia},
booktitle={First IEEE Conference on Secure and Trustworthy Machine Learning},
year={2023},
url={https://openreview.net/forum?id=I03uWXMi6oD}
}

@inproceedings{lee2019set,
  title={Set transformer: A framework for attention-based permutation-invariant neural networks},
  author={Lee, Juho and Lee, Yoonho and Kim, Jungtaek and Kosiorek, Adam and Choi, Seungjin and Teh, Yee Whye},
  booktitle={International conference on machine learning},
  pages={3744--3753},
  year={2019},
  organization={PMLR}
}

%% AL Limitation
@article{lowell2018practical,
  title={Practical obstacles to deploying active learning},
  author={Lowell, David and Lipton, Zachary C and Wallace, Byron C},
  journal={arXiv preprint arXiv:1807.04801},
  year={2018}
}

@article{siddhant2018deep,
  title={Deep bayesian active learning for natural language processing: Results of a large-scale empirical study},
  author={Siddhant, Aditya and Lipton, Zachary C},
  journal={arXiv preprint arXiv:1808.05697},
  year={2018}
}


%% AL application
@inproceedings{song2020mirrored,
  title={Mirrored Plasmonic Filter Design via Active Learning of Multi-Fidelity Physical Models},
  author={Song, Jialin and Tokpanov, Yury S and Chen, Yuxin and Fleischman, Dagny and Fountaine, Katherine T and Yue, Yisong and Atwater, Harry A},
  booktitle={CLEO: Science and Innovations},
  pages={JTu2D--6},
  year={2020},
  organization={Optica Publishing Group}
}

%% protein design
@inproceedings{yang2019batched,
  title={Batched stochastic Bayesian optimization via combinatorial constraints design},
  author={Yang, Kevin K and Chen, Yuxin and Lee, Alycia and Yue, Yisong},
  booktitle={The 22nd International Conference on Artificial Intelligence and Statistics},
  pages={3410--3419},
  year={2019},
  organization={PMLR}
}

%% LHC
@article{mahesh2021towards,
  title={Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities},
  author={Mahesh, Chinmaya and Dona, Kristin and Miller, David W and Chen, Yuxin},
  journal={arXiv preprint arXiv:2104.06622},
  year={2021}
}

%% task-specific
@article{jia2019efficient,
  title={Efficient task-specific data valuation for nearest neighbor algorithms},
  author={Jia, Ruoxi and Dao, David and Wang, Boxin and Hubis, Frances Ann and Gurel, Nezihe Merve and Li, Bo and Zhang, Ce and Spanos, Costas J and Song, Dawn},
  journal={arXiv preprint arXiv:1908.08619},
  year={2019}
}

@inproceedings{nath2021power,
  title={The power of proxy data and proxy networks for hyper-parameter optimization in medical image segmentation},
  author={Nath, Vishwesh and Yang, Dong and Hatamizadeh, Ali and Abidin, Anas A and Myronenko, Andriy and Roth, Holger R and Xu, Daguang},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2021: 24th International Conference, Strasbourg, France, September 27--October 1, 2021, Proceedings, Part III 24},
  pages={456--465},
  year={2021},
  organization={Springer}
}

% shapley

@inproceedings{ghorbani2022data,
  title={Data shapley valuation for efficient batch active learning},
  author={Ghorbani, Amirata and Zou, James and Esteva, Andre},
  booktitle={2022 56th Asilomar Conference on Signals, Systems, and Computers},
  pages={1456--1462},
  year={2022},
  organization={IEEE}
}



%% FISHER computationally expensive
@article{ash2021gone,
  title={Gone fishing: Neural active learning with fisher embeddings},
  author={Ash, Jordan and Goel, Surbhi and Krishnamurthy, Akshay and Kakade, Sham},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={8927--8939},
  year={2021}
}


%% applications for few rounds AL
@article{botu2015adaptive,
  title={Adaptive machine learning framework to accelerate ab initio molecular dynamics},
  author={Botu, Venkatesh and Ramprasad, Rampi},
  journal={International Journal of Quantum Chemistry},
  volume={115},
  number={16},
  pages={1074--1083},
  year={2015},
  publisher={Wiley Online Library}
}

@article{yang2019machine,
  title={Machine-learning-guided directed evolution for protein engineering},
  author={Yang, Kevin K and Wu, Zachary and Arnold, Frances H},
  journal={Nature methods},
  volume={16},
  number={8},
  pages={687--694},
  year={2019},
  publisher={Nature Publishing Group US New York}
}

@article{sharp2011prompt,
  title={Are the prompt responders to an online panel survey different from those who respond later?},
  author={Sharp, Anne and Moore, Patrick and Anderson, Katherine},
  journal={Australasian Journal of Market \& Social Research},
  volume={19},
  number={1},
  year={2011}
}


@article{mussmann2022active,
  title={Active Learning with Expected Error Reduction},
  author={Mussmann, Stephen and Reisler, Julia and Tsai, Daniel and Mousavi, Ehsan and O'Brien, Shayne and Goldszmidt, Moises},
  journal={arXiv preprint arXiv:2211.09283},
  year={2022}
}

@article{roy2001toward,
  title={Toward optimal active learning through monte carlo estimation of error reduction},
  author={Roy, Nicholas and McCallum, Andrew},
  journal={ICML, Williamstown},
  volume={2},
  pages={441--448},
  year={2001}
}

%% Mutual information
@article{sourati2016classification,
  title={Classification active learning based on mutual information},
  author={Sourati, Jamshid and Akcakaya, Murat and Dy, Jennifer G and Leen, Todd K and Erdogmus, Deniz},
  journal={Entropy},
  volume={18},
  number={2},
  pages={51},
  year={2016},
  publisher={MDPI}
}

@article{adaimi2019leveraging,
  title={Leveraging active learning and conditional mutual information to minimize data annotation in human activity recognition},
  author={Adaimi, Rebecca and Thomaz, Edison},
  journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume={3},
  number={3},
  pages={1--23},
  year={2019},
  publisher={ACM New York, NY, USA}
}

@article{lindley1956measure,
  title={On a measure of the information provided by an experiment},
  author={Lindley, Dennis V},
  journal={The Annals of Mathematical Statistics},
  volume={27},
  number={4},
  pages={986--1005},
  year={1956},
  publisher={Institute of Mathematical Statistics}
}


@inproceedings{chen2015submodular,
  title={Submodular surrogates for value of information},
  author={Chen, Yuxin and Javdani, Shervin and Karbasi, Amin and Bagnell, J and Srinivasa, Siddhartha and Krause, Andreas},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={29},
  number={1},
  year={2015}
}

@article{jackson2019value,
  title={Value of information: Sensitivity analysis and research design in Bayesian evidence synthesis},
  author={Jackson, Christopher and Presanis, Anne and Conti, Stefano and De Angelis, Daniela},
  journal={Journal of the American Statistical Association},
  volume={114},
  number={528},
  pages={1436--1449},
  year={2019},
  publisher={Taylor \& Francis}
}


@inproceedings{jia2019towards,
  title={Towards efficient data valuation based on the shapley value},
  author={Jia, Ruoxi and Dao, David and Wang, Boxin and Hubis, Frances Ann and Hynes, Nick and G{\"u}rel, Nezihe Merve and Li, Bo and Zhang, Ce and Song, Dawn and Spanos, Costas J},
  booktitle={The 22nd International Conference on Artificial Intelligence and Statistics},
  pages={1167--1176},
  year={2019},
  organization={PMLR}
}

@inproceedings{jia2021scalability,
  title={Scalability vs. utility: Do we have to sacrifice one for the other in data importance quantification?},
  author={Jia, Ruoxi and Wu, Fan and Sun, Xuehui and Xu, Jiacen and Dao, David and Kailkhura, Bhavya and Zhang, Ce and Li, Bo and Song, Dawn},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8239--8247},
  year={2021}
}

@inproceedings{
just2023lava,
title={{LAVA}: Data Valuation without Pre-Specified Learning Algorithms},
author={Hoang Anh Just and Feiyang Kang and Tianhao Wang and Yi Zeng and Myeongseob Ko and Ming Jin and Ruoxi Jia},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=JJuP86nBl4q}
}

@inproceedings{killamsetty2021glister,
  title={Glister: Generalization based data subset selection for efficient and robust learning},
  author={Killamsetty, Krishnateja and Sivasubramanian, Durga and Ramakrishnan, Ganesh and Iyer, Rishabh},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={9},
  pages={8110--8118},
  year={2021}
}

@book{farahani2009facility,
  title={Facility location: concepts, models, algorithms and case studies},
  author={Farahani, Reza Zanjirani and Hekmatfar, Masoud},
  year={2009},
  publisher={Springer Science \& Business Media}
}

@inproceedings{franceschi2018bilevel,
  title={Bilevel programming for hyperparameter optimization and meta-learning},
  author={Franceschi, Luca and Frasconi, Paolo and Salzo, Saverio and Grazzi, Riccardo and Pontil, Massimiliano},
  booktitle={International Conference on Machine Learning},
  pages={1568--1577},
  year={2018},
  organization={PMLR}
}

@article{zaheer2017deep,
  title={Deep sets},
  author={Zaheer, Manzil and Kottur, Satwik and Ravanbakhsh, Siamak and Poczos, Barnabas and Salakhutdinov, Russ R and Smola, Alexander J},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}


@article{boros2002pseudo,
  title={Pseudo-boolean optimization},
  author={Boros, Endre and Hammer, Peter L},
  journal={Discrete applied mathematics},
  volume={123},
  number={1-3},
  pages={155--225},
  year={2002},
  publisher={Elsevier}
}

@inproceedings{grazzi2020iteration,
  title={On the Iteration Complexity of Hypergradient Computation},
  author={Grazzi, Riccardo and Franceschi, Luca and Pontil, Massimiliano and Salzo, Saverio},
  journal={Thirty-seventh International Conference on Machine Learning (ICML)},
  year={2020}
}


@article{colson2007overview,
  title={An overview of bilevel optimization},
  author={Colson, Beno{\^\i}t and Marcotte, Patrice and Savard, Gilles},
  journal={Annals of operations research},
  volume={153},
  pages={235--256},
  year={2007},
  publisher={Springer}
}


%%% learning to rank
@inproceedings{li2021learning,
  title={Learning to rank for active learning: A listwise approach},
  author={Li, Minghan and Liu, Xialei and van de Weijer, Joost and Raducanu, Bogdan},
  booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
  pages={5587--5594},
  year={2021},
  organization={IEEE}
}

@article{liu2009learning,
  title={Learning to rank for information retrieval},
  author={Liu, Tie-Yan and others},
  journal={Foundations and Trends{\textregistered} in Information Retrieval},
  volume={3},
  number={3},
  pages={225--331},
  year={2009},
  publisher={Now Publishers, Inc.}
}

@book{li2022learning,
  title={Learning to rank for information retrieval and natural language processing},
  author={Li, Hang},
  year={2022},
  publisher={Springer Nature}
}

@inproceedings{karatzoglou2013learning,
  title={Learning to rank for recommender systems},
  author={Karatzoglou, Alexandros and Baltrunas, Linas and Shi, Yue},
  booktitle={Proceedings of the 7th ACM Conference on Recommender Systems},
  pages={493--494},
  year={2013}
}

@inproceedings{burges2005learning,
  title={Learning to rank using gradient descent},
  author={Burges, Chris and Shaked, Tal and Renshaw, Erin and Lazier, Ari and Deeds, Matt and Hamilton, Nicole and Hullender, Greg},
  booktitle={Proceedings of the 22nd international conference on Machine learning},
  pages={89--96},
  year={2005}
}

@article{kirsch2021stochastic,
  title={Stochastic batch acquisition for deep active learning},
  author={Kirsch, Andreas and Farquhar, Sebastian and Atighehchian, Parmida and Jesson, Andrew and Branchaud-Charron, Frederic and Gal, Yarin},
  journal={arXiv preprint arXiv:2106.12059},
  year={2021}
}

@inproceedings{saran2023streaming,
author = {Saran, Akanksha and Yousefi, Safoora and Krishnamurthy, Akshay and Langford, John and Ash, Jordan},
title = {Streaming Active Learning with Deep Neural Networks},
booktitle = {ICML 2023},
year = {2023},
month = {March},
abstract = {Active learning is perhaps most naturally posed as an online learning problem. However, prior active learning approaches with deep neural networks assume offline access to the entire dataset ahead of time. This paper proposes VeSSAL, a new algorithm for batch active learning with deep neural networks in streaming settings, which samples groups of points to query for labels at the moment they are encountered. Our approach trades off between uncertainty and diversity of queried samples to match a desired query rate without requiring any hand-tuned hyperparameters. Altogether, we expand the applicability of deep neural networks to realistic active learning scenarios, such as applications relevant to HCI and large, fractured datasets.}
}
%url = {https://www.microsoft.com/en-us/research/publication/streaming-active-learning-with-deep-neural-networks/},
}

@article{park2023trak,
  title={Trak: Attributing model behavior at scale},
  author={Park, Sung Min and Georgiev, Kristian and Ilyas, Andrew and Leclerc, Guillaume and Madry, Aleksander},
  journal={arXiv preprint arXiv:2303.14186},
  year={2023}
}


@article{ko1995exact,
  title={An exact algorithm for maximum entropy sampling},
  author={Ko, Chun-Wa and Lee, Jon and Queyranne, Maurice},
  journal={Operations Research},
  volume={43},
  number={4},
  pages={684--691},
  year={1995},
  publisher={INFORMS}
}

@inproceedings{chen2015sequential,
  title={Sequential information maximization: When is greedy near-optimal?},
  author={Chen, Yuxin and Hassani, S Hamed and Karbasi, Amin and Krause, Andreas},
  booktitle={Conference on Learning Theory},
  pages={338--363},
  year={2015},
  organization={PMLR}
}

@article{
anonymous2023oneround,
title={One-Round Active Learning through Data Utility Learning and Proxy Models},
author={Anonymous},
journal={Submitted to Transactions on Machine Learning Research},
year={2023},
url={https://openreview.net/forum?id=8HQCOMRa7g},
note={Under review}
}

@article{altschuler2017near,
  title={Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration},
  author={Altschuler, Jason and Niles-Weed, Jonathan and Rigollet, Philippe},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}


@article{verma2022interpolation,
  title={Interpolation consistency training for semi-supervised learning},
  author={Verma, Vikas and Kawaguchi, Kenji and Lamb, Alex and Kannala, Juho and Solin, Arno and Bengio, Yoshua and Lopez-Paz, David},
  journal={Neural Networks},
  volume={145},
  pages={90--106},
  year={2022},
  publisher={Elsevier}
}

@inproceedings{jeong2021interpolation,
  title={Interpolation-based semi-supervised learning for object detection},
  author={Jeong, Jisoo and Verma, Vikas and Hyun, Minsung and Kannala, Juho and Kwak, Nojun},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11602--11611},
  year={2021}
}

@inproceedings{roth2006margin,
  title={Margin-based active learning for structured output spaces},
  author={Roth, Dan and Small, Kevin},
  booktitle={Machine Learning: ECML 2006: 17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006 Proceedings 17},
  pages={413--424},
  year={2006},
  organization={Springer}
}

@article{lecun1998gradient,
  title={Gradient-based learning applied to document recognition},
  author={LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
  journal={Proceedings of the IEEE},
  volume={86},
  number={11},
  pages={2278--2324},
  year={1998},
  publisher={Ieee}
}

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@article{beck2021effective,
  title={Effective evaluation of deep active learning on image classification tasks},
  author={Beck, Nathan and Sivasubramanian, Durga and Dani, Apurva and Ramakrishnan, Ganesh and Iyer, Rishabh},
  journal={arXiv preprint arXiv:2106.15324},
  year={2021}
}

@article{netzer2011reading,
  title={Reading digits in natural images with unsupervised feature learning},
  author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y},
  year={2011}
}

@article{krizhevsky2009learning,
  title={Learning multiple layers of features from tiny images},
  author={Krizhevsky, Alex and Hinton, Geoffrey and others},
  year={2009},
  publisher={Toronto, ON, Canada}
}



@article{xiao2017fashion,
  title={Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms},
  author={Xiao, Han and Rasul, Kashif and Vollgraf, Roland},
  journal={arXiv preprint arXiv:1708.07747},
  year={2017}
}

@misc{decileteam_2023_distil,
    author = {Decile Team},
    title = {distil},
    year = {2023}, 
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/decile-team/distil}},
    commit = {full commit hash} 
}


@article{paszke2017automatic,
  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}
}


@inproceedings{borsos2021semi,
  title={Semi-supervised batch active learning via bilevel optimization},
  author={Borsos, Zal{\'a}n and Tagliasacchi, Marco and Krause, Andreas},
  booktitle={ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={3495--3499},
  year={2021},
  organization={IEEE}
}

@inproceedings{jiang2021assessing,
  title={Assessing Generalization of SGD via Disagreement},
  author={Jiang, Yiding and Nagarajan, Vaishnavh and Baek, Christina and Kolter, J Zico},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

@article{d2022underspecification,
  title={Underspecification presents challenges for credibility in modern machine learning},
  author={D'Amour, Alexander and Heller, Katherine and Moldovan, Dan and Adlam, Ben and Alipanahi, Babak and Beutel, Alex and Chen, Christina and Deaton, Jonathan and Eisenstein, Jacob and Hoffman, Matthew D and others},
  journal={The Journal of Machine Learning Research},
  volume={23},
  number={1},
  pages={10237--10297},
  year={2022},
  publisher={JMLRORG}
}

@inproceedings{zhong2021larger,
  title={Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level},
  author={Zhong, Ruiqi and Ghosh, Dhruba and Klein, Dan and Steinhardt, Jacob},
  booktitle={Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021},
  pages={3813--3827},
  year={2021}
}

@article{rajeswaran2019meta,
  title={Meta-learning with implicit gradients},
  author={Rajeswaran, Aravind and Finn, Chelsea and Kakade, Sham M and Levine, Sergey},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@article{liu2019self,
  title={Self-supervised generalisation with meta auxiliary learning},
  author={Liu, Shikun and Davison, Andrew and Johns, Edward},
  journal={Advances in Neural Information Processing Systems},
  volume={32},
  year={2019}
}

@article{raghu2021meta,
  title={Meta-learning to improve pre-training},
  author={Raghu, Aniruddh and Lorraine, Jonathan and Kornblith, Simon and McDermott, Matthew and Duvenaud, David K},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={23231--23244},
  year={2021}
}

@inproceedings{maclaurin2015gradient,
  title={Gradient-based hyperparameter optimization through reversible learning},
  author={Maclaurin, Dougal and Duvenaud, David and Adams, Ryan},
  booktitle={International conference on machine learning},
  pages={2113--2122},
  year={2015},
  organization={PMLR}
}


@article{ouyang2022training,
  title={Training language models to follow instructions with human feedback},
  author={Ouyang, Long and Wu, Jeffrey and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and others},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  pages={27730--27744},
  year={2022}
}


@article{engstrom2024dsdm,
  title={DsDm: Model-Aware Dataset Selection with Datamodels},
  author={Engstrom, Logan and Feldmann, Axel and Madry, Aleksander},
  journal={arXiv preprint arXiv:2401.12926},
  year={2024}
}

@inproceedings{parvaneh2022active,
  title={Active learning by feature mixing},
  author={Parvaneh, Amin and Abbasnejad, Ehsan and Teney, Damien and Haffari, Gholamreza Reza and Van Den Hengel, Anton and Shi, Javen Qinfeng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12237--12246},
  year={2022}
}

@article{wang2023one,
  title={One-Round Active Learning through Data Utility Learning and Proxy Models},
  author={Wang, Jiachen T and Chen, Si and Jia, Ruoxi},
  journal={Transactions on Machine Learning Research},
  year={2023}
}

@inproceedings{yuan2021multiple,
  title={Multiple instance active learning for object detection},
  author={Yuan, Tianning and Wan, Fang and Fu, Mengying and Liu, Jianzhuang and Xu, Songcen and Ji, Xiangyang and Ye, Qixiang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5330--5339},
  year={2021}
}

@article{chaplot2021seal,
  title={Seal: Self-supervised embodied active learning using exploration and 3d consistency},
  author={Chaplot, Devendra Singh and Dalal, Murtaza and Gupta, Saurabh and Malik, Jitendra and Salakhutdinov, Russ R},
  journal={Advances in neural information processing systems},
  volume={34},
  pages={13086--13098},
  year={2021}
}


@inproceedings{kim2021task,
  title={Task-aware variational adversarial active learning},
  author={Kim, Kwanyoung and Park, Dongwon and Kim, Kwang In and Chun, Se Young},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8166--8175},
  year={2021}
}

@inproceedings{hacohen2022active,
  title={Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets},
  author={Hacohen, Guy and Dekel, Avihu and Weinshall, Daphna},
  booktitle={International Conference on Machine Learning},
  pages={8175--8195},
  year={2022},
  organization={PMLR}
}