@string{AAAI = {Proc. Assoc. Adv. Art. Intell. (AAAI)}}

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@string{eccv = {Euro. Conf. Comp. Vision}}

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@string{iclr = {Proc. Int. Conf. Learning Representations}}

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@string{ieee_j_ip = {{IEEE} Trans. Image Proc.}}

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@string{ijcv = {Intl. J. Comp. Vision}}

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@string{ipsn = {Proc. Int. Symp. Inform. Proc. Sensor Net. (IPSN)}}

@string{iscas = {Proc. IEEE Int. Symp. Circ. Sys. (ISCAS)}}

@string{isit = {Proc. IEEE Int. Symp. Inform. Theory (ISIT)}}

@string{ismrm = {Proc. Annual Meeting of ISMRM}}

@string{ita = {Proc. Work. Inform. Theory and Applications (ITA)}}

@string{jams = {J. Amer. Math. Soc.}}

@string{jan = {Jan.}}

@string{jasa = {J. Amer. Statist. Assoc.}}

@string{jcomp = {J. Complexity}}

@string{jmlr = {J. Machine Learning Research}}

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@string{neurips = {Adv. Neural Inf. Proc. Sys. (NeurIPS)}}

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@string{oct = {Oct.}}

@string{pnas = {Proc. Natl. Acad. Sci.}}

@string{pods = {Proc. Symp. Principles of Database Systems (PODS)}}

@string{pr = {Pattern Recognition}}

@string{preprint = {Preprint}}

@string{sampta = {Proc. Sampling Theory and Appl. (SampTA)}}

@string{sda = {Proc. Symp. Discrete Alg.}}

@string{sep = {Sept.}}

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@string{siamimagsci = {SIAM J. Imag. Sci.}}

@string{siamrev = {SIAM Rev.}}

@string{soda = {Proc. ACM Symp. Discrete Alg. (SODA)}}

@string{spars = {{Proc. Work. Struc. Parc. Rep. Adap. Signaux (SPARS)}}}

@string{spiecompimag = {Proc. IS{\&T}/SPIE Symp. Elec. Imag.: Comp. Imag.}}

@string{spieradar = {Proc. SPIE Radar Sensor Tech.}}

@string{spiewaveletapps = {Proc. SPIE Ann. Meeting: Wavelet Apps. Signal Imag. Proc.}}

@string{spiewavelets = {Proc. SPIE Optics Photonics: Wavelets}}

@string{spl = {IEEE Sig. Proc. Lett.}}

@string{spmag = {IEEE Sig. Proc. Mag.}}

@string{ssp = {Proc. IEEE Work. Stat. Signal Proc.}}

@string{stoc = {Proc. ACM Symp. Theory of Comput.}}

@string{taes = {IEEE Trans. Aerospace Elec. Systems}}

@string{tassp = {IEEE Trans. Acoust., Speech, Signal Proc.}}

@string{tip = {IEEE Trans. Image Proc.}}

@string{tip_toappear = {to appear in {\em IEEE Trans. Image Proc.}}}

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@string{tit_toappear = {to appear in {\em IEEE Trans. Inform. Theory}}}

@string{tmi = {IEEE Trans. Med. Imaging}}

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@string{tog = {ACM Trans. Graph.}}

@string{tpami = {IEEE Trans. Pattern Anal. Machine Intell.}}

@string{tsp = {IEEE Trans. Sig. Proc.}}

@article{vaccine,
  title={COVID-19 Vaccine: A comprehensive status report},
  author={Kaur, Simran Preet and Gupta, Vandana},
  journal={Virus research},
  volume={288},
  pages={198114},
  year={2020},
  publisher={Elsevier}
}

@article{medical_survey,
  title={Transfer learning for non-image data in clinical research: a scoping review},
  author={Ebbehoj, Andreas and Thunbo, Mette {\O}stergaard and Andersen, Ole Emil and Glindtvad, Michala Vilstrup and Hulman, Adam},
  journal={PLOS Digital Health},
  volume={1},
  number={2},
  pages={e0000014},
  year={2022},
  publisher={Public Library of Science San Francisco, CA USA}
}

@book{Thrun1998,
  title={Learning to learn},
  author={Thrun, Sebastian and Pratt, Lorien},
  year={2012},
  publisher={Springer Science \& Business Media}
}

@inproceedings{Blum1998,
  title={Combining labeled and unlabeled data with co-training},
  author={Avrim Blum and Tom. Mitchell},
  booktitle={COLT' 98},
  year={1998}
}



@article{Yosinski2014,
  title={How transferable are features in deep neural networks?},
  author={Yosinski, Jason and Clune, Jeff and Bengio, Yoshua and Lipson, Hod},
  journal={Advances in neural information processing systems},
  volume={27},
  year={2014}
}

@ARTICLE{TLsurvey2008,
  author={Pan, Sinno Jialin and Yang, Qiang},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={A Survey on Transfer Learning}, 
  year={2010},
  volume={22},
  number={10},
  pages={1345-1359},
  doi={10.1109/TKDE.2009.191}}

@ARTICLE{TLsurvey2021,
  author={Zhuang, Fuzhen and Qi, Zhiyuan and Duan, Keyu and Xi, Dongbo and Zhu, Yongchun and Zhu, Hengshu and Xiong, Hui and He, Qing},
  journal={Proceedings of the IEEE}, 
  title={A Comprehensive Survey on Transfer Learning}, 
  year={2021},
  volume={109},
  number={1},
  pages={43-76},
  doi={10.1109/JPROC.2020.3004555}}

@article{Arjovsky2019,
  title={Invariant risk minimization},
  author={Arjovsky, Martin and Bottou, L{\'e}on and Gulrajani, Ishaan and Lopez-Paz, David},
  journal={arXiv preprint arXiv:1907.02893},
  year={2019}
}

@article{geirhos2018,
  title={ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness},
  author={Geirhos, Robert and Rubisch, Patricia and Michaelis, Claudio and Bethge, Matthias and Wichmann, Felix A and Brendel, Wieland},
  journal={arXiv preprint arXiv:1811.12231},
  year={2018}
}

@article{heat_equation,
  title={Newton's law of cooling},
  author={Winterton, RHS},
  journal={Contemporary Physics},
  volume={40},
  number={3},
  pages={205--212},
  year={1999},
  publisher={Taylor \& Francis}
}

@article{TLNLPsurvey,
  title={A survey on transfer learning in natural language processing},
  author={Alyafeai, Zaid and AlShaibani, Maged Saeed and Ahmad, Irfan},
  journal={arXiv preprint arXiv:2007.04239},
  year={2020}
}

@article{TLCVsurvey,
  title={Deep visual domain adaptation: A survey},
  author={Wang, Mei and Deng, Weihong},
  journal={Neurocomputing},
  volume={312},
  pages={135--153},
  year={2018},
  publisher={Elsevier}
}

@article{JobsDataset,
  title={Evaluating the econometric evaluations of training programs with experimental data},
  author={LaLonde, Robert J},
  journal={The American economic review},
  pages={604--620},
  year={1986},
  publisher={JSTOR}
}

@inproceedings{Beery2018,
  title={Recognition in terra incognita},
  author={Beery, Sara and Van Horn, Grant and Perona, Pietro},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={456--473},
  year={2018}
}



@inproceedings{jamieson2016non,
  title={Non-stochastic best arm identification and hyperparameter optimization},
  author={Jamieson, Kevin and Talwalkar, Ameet},
  booktitle={Artificial Intelligence and Statistics},
  pages={240--248},
  year={2016}
}

@article{li2018massively,
  title={Massively parallel hyperparameter tuning},
  author={Li, Liam and Jamieson, Kevin and Rostamizadeh, Afshin and Gonina, Ekaterina and Hardt, Moritz and Recht, Benjamin and Talwalkar, Ameet},
  journal={arXiv preprint arXiv:1810.05934},
  year={2018}
}

@article{nayman2019xnas,
  title={XNAS: Neural Architecture Search with Expert Advice},
  author={Nayman, Niv and Noy, Asaf and Ridnik, Tal and Friedman, Itamar and Jin, Rong and Zelnik-Manor, Lihi},
  journal=neurips,
  year={2019}
}

@article{ha2016hypernetworks,
  title={Hypernetworks},
  author={Ha, David and Dai, Andrew and Le, Quoc V},
  journal={arXiv preprint arXiv:1609.09106},
  year={2016}
}

@article{hu2019efficient,
  title={Efficient Forward Architecture Search},
  author={Hu, Hanzhang and Langford, John and Caruana, Rich and Mukherjee, Saurajit and Horvitz, Eric and Dey, Debadeepta},
  journal=neurips,
  year={2019}
}

@article{noy2019asap,
  title={ASAP: Architecture search, anneal and prune},
  author={Noy, Asaf and Nayman, Niv and Ridnik, Tal and Zamir, Nadav and Doveh, Sivan and Friedman, Itamar and Giryes, Raja and Zelnik-Manor, Lihi},
  journal={arXiv preprint arXiv:1904.04123},
  year={2019}
}


@article{li2017hyperband,
  title={Hyperband: A novel bandit-based approach to hyperparameter optimization},
  author={Li, Lisha and Jamieson, Kevin and DeSalvo, Giulia and Rostamizadeh, Afshin and Talwalkar, Ameet},
  journal={The Journal of Machine Learning Research},
  volume={18},
  number={1},
  pages={6765--6816},
  year={2017},
  publisher={JMLR. org}
}

@article{hazan2017hyperparameter,
  title={Hyperparameter optimization: a spectral approach},
  author={Hazan, Elad and Klivans, Adam and Yuan, Yang},
  journal={arXiv preprint arXiv:1706.00764},
  year={2017}
}

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

@article{tibshirani1996regression,
  title={Regression shrinkage and selection via the lasso},
  author={Tibshirani, Robert},
  journal={Journal of the Royal Statistical Society. Series B (Methodological)},
  pages={267--288},
  year={1996},
  publisher={JSTOR}
}

@article{yuan2006model,
  title={Model selection and estimation in regression with grouped variables},
  author={Yuan, Ming and Lin, Yi},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={68},
  number={1},
  pages={49--67},
  year={2006},
  publisher={Wiley Online Library}
}

@article{kumar2018parallel,
  title={Parallel Architecture and Hyperparameter Search via Successive Halving and Classification},
  author={Kumar, Manoj and Dahl, George E and Vasudevan, Vijay and Norouzi, Mohammad},
  journal={arXiv preprint arXiv:1805.10255},
  year={2018}
}

@inproceedings{snoek2012practical,
  title={Practical bayesian optimization of machine learning algorithms},
  author={Snoek, Jasper and Larochelle, Hugo and Adams, Ryan P},
  booktitle={Advances in neural information processing systems},
  pages={2951--2959},
  year={2012}
}

@article{bergstra2012random,
  title={Random search for hyper-parameter optimization},
  author={Bergstra, James and Bengio, Yoshua},
  journal={Journal of Machine Learning Research},
  volume={13},
  number={Feb},
  pages={281--305},
  year={2012}
}

@inproceedings{bergstra2011algorithms,
  title={Algorithms for hyper-parameter optimization},
  author={Bergstra, James S and Bardenet, R{\'e}mi and Bengio, Yoshua and K{\'e}gl, Bal{\'a}zs},
  booktitle={Advances in neural information processing systems},
  pages={2546--2554},
  year={2011}
}

@article{cubuk2018autoaugment,
  title={Autoaugment: Learning augmentation policies from data},
  author={Cubuk, Ekin D and Zoph, Barret and Mane, Dandelion and Vasudevan, Vijay and Le, Quoc V},
  journal=cvpr,
  year={2019}
}


@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}
}

@article{luketina2015scalable,
  title={Scalable gradient-based tuning of continuous regularization hyperparameters},
  author={Luketina, Jelena and Berglund, Mathias and Greff, Klaus and Raiko, Tapani},
  journal={arXiv preprint arXiv:1511.06727},
  year={2015}
}

@article{falkner2018bohb,
  title={BOHB: Robust and efficient hyperparameter optimization at scale},
  author={Falkner, Stefan and Klein, Aaron and Hutter, Frank},
  journal={arXiv preprint arXiv:1807.01774},
  year={2018}
}

@article{bertrandhyperparameter,
  title={Hyperparameter Optimization of Deep Neural Networks: Combining Hyperband with Bayesian Model Selection},
  author={Bertrand, Hadrien and Ardon, Roberto and Perrot, Matthieu and Bloch, Isabelle},
  year={2017}
}

@article{wang2018combination,
  title={Combination of Hyperband and Bayesian Optimization for Hyperparameter Optimization in Deep Learning},
  author={Wang, Jiazhuo and Xu, Jason and Wang, Xuejun},
  journal={arXiv preprint arXiv:1801.01596},
  year={2018}
}

@inproceedings{snoek2014input,
  title={Input warping for bayesian optimization of non-stationary functions},
  author={Snoek, Jasper and Swersky, Kevin and Zemel, Rich and Adams, Ryan},
  booktitle=icml,
  pages={1674--1682},
  year={2014}
}

@inproceedings{hutter2011sequential,
  title={Sequential model-based optimization for general algorithm configuration},
  author={Hutter, Frank and Hoos, Holger H and Leyton-Brown, Kevin},
  booktitle={International Conference on Learning and Intelligent Optimization},
  pages={507--523},
  year={2011},
  organization={Springer}
}

@article{haviv2016list,
  title={The list-decoding size of Fourier-sparse boolean functions},
  author={Haviv, Ishay and Regev, Oded},
  journal={ACM Transactions on Computation Theory (TOCT)},
  volume={8},
  number={3},
  pages={10},
  year={2016},
  publisher={ACM}
}

@inproceedings{eggensperger2013towards,
  title={Towards an empirical foundation for assessing bayesian optimization of hyperparameters},
  author={Eggensperger, Katharina and Feurer, Matthias and Hutter, Frank and Bergstra, James and Snoek, Jasper and Hoos, Holger and Leyton-Brown, Kevin},
  booktitle={NIPS workshop on Bayesian Optimization in Theory and Practice},
  volume={10},
  pages={3},
  year={2013}
}

@inproceedings{thornton2013auto,
  title={Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms},
  author={Thornton, Chris and Hutter, Frank and Hoos, Holger H and Leyton-Brown, Kevin},
  booktitle={Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining},
  pages={847--855},
  year={2013},
  organization={ACM}
}

@inproceedings{ilievski2017efficient,
  title={Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates.},
  author={Ilievski, Ilija and Akhtar, Taimoor and Feng, Jiashi and Shoemaker, Christine Annette},
  booktitle={AAAI},
  pages={822--829},
  year={2017}
}

@article{fu2016drmad,
  title={Drmad: Distilling reverse-mode automatic differentiation for optimizing hyperparameters of deep neural networks},
  author={Fu, Jie and Luo, Hongyin and Feng, Jiashi and Low, Kian Hsiang and Chua, Tat-Seng},
  journal={arXiv preprint arXiv:1601.00917},
  year={2016}
}

@article{bengio2000gradient,
  title={Gradient-based optimization of hyperparameters},
  author={Bengio, Yoshua},
  journal={Neural computation},
  volume={12},
  number={8},
  pages={1889--1900},
  year={2000},
  publisher={MIT Press}
}

@article{franceschi2017forward,
  title={Forward and reverse gradient-based hyperparameter optimization},
  author={Franceschi, Luca and Donini, Michele and Frasconi, Paolo and Pontil, Massimiliano},
  journal={arXiv preprint arXiv:1703.01785},
  year={2017}
}

@article{li2019random,
  title={Random search and reproducibility for neural architecture search},
  author={Li, Liam and Talwalkar, Ameet},
  journal={arXiv preprint arXiv:1902.07638},
  year={2019}
}

@article{cai2018proxylessnas,
  title={ProxylessNAS: Direct neural architecture search on target task and hardware},
  author={Cai, Han and Zhu, Ligeng and Han, Song},
  journal=iclr,
  year={2019}
}

@article{liu2018darts,
  title={Darts: Differentiable architecture search},
  author={Liu, Hanxiao and Simonyan, Karen and Yang, Yiming},
  journal=icml,
  year={2018}
}

@article{devries2017improved,
  title={Improved regularization of convolutional neural networks with cutout},
  author={DeVries, Terrance and Taylor, Graham W},
  journal={arXiv preprint arXiv:1708.04552},
  year={2017}
}

@article{zoph2018learning,
  title={Learning transferable architectures for scalable image recognition},
  author={Zoph, Barret and Vasudevan, Vijay and Shlens, Jonathon and Le, Quoc V},
  journal=cvpr,
  year={2018}
}

@article{real2018regularized,
  title={Regularized evolution for image classifier architecture search},
  author={Real, Esteban and Aggarwal, Alok and Huang, Yanping and Le, Quoc V},
  journal=AAAI,
  year={2019}
}

@article{pham2018efficient,
  title={Efficient neural architecture search via parameter sharing},
  author={Pham, Hieu and Guan, Melody Y and Zoph, Barret and Le, Quoc V and Dean, Jeff},
  journal=icml,
  year={2018}
}

@article{stobbe2012learning,
  title={Learning fourier sparse set functions},
  author={Stobbe, Peter and Krause, Andreas},
  journal=aistats,
  year={2012}
}
@article{cho2019reducing,
  title={Reducing the Search Space for Hyperparameter Optimization Using Group Sparsity},
  author={Cho, Minsu and Hegde, Chinmay},
  journal=icassp,
  year={2019}
}

@article{bender2018understanding,
  title={Understanding and simplifying one-shot architecture search},
  author={Bender, Gabriel and Kindermans, Pieter-Jan and Zoph, Barret and Vasudevan, Vijay and Le, Quoc},
  journal=icml,
  year={2018}
}

@article{hoffer2017train,
  title={Train longer, generalize better: closing the generalization gap in large batch training of neural networks},
  author={Hoffer, Elad and Hubara, Itay and Soudry, Daniel},
  journal = neurips,
  year={2017}
}

@article{zilly2017recurrent,
  title={Recurrent highway networks},
  author={Zilly, Julian Georg and Srivastava, Rupesh Kumar and Koutn{\'\i}k, Jan and Schmidhuber, J{\"u}rgen},
  journal=icml,
  year={2017}
}

@inproceedings{9054118,
  title={Supervised Encoding for Discrete Representation Learning},
  author={Le, Cat P and Zhou, Yi and Ding, Jie and Tarokh, Vahid},
  booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={3447--3451},
  year={2020},
  organization={IEEE}
}

@article{merity2017regularizing,
  title={Regularizing and optimizing LSTM language models},
  author={Merity, Stephen and Keskar, Nitish Shirish and Socher, Richard},
  journal=iclr,
  year={2018}
}

@article{yang2017breaking,
  title={Breaking the softmax bottleneck: A high-rank RNN language model},
  author={Yang, Zhilin and Dai, Zihang and Salakhutdinov, Ruslan and Cohen, William W},
  journal=iclr,
  year={2018}
}

@article{zoph2016neural,
  title={Neural architecture search with reinforcement learning},
  author={Zoph, Barret and Le, Quoc V},
  journal=iclr,
  year={2017}
}
@article{yamada2018shakedrop,
  title={ShakeDrop Regularization for Deep Residual Learning},
  author={Yamada, Yoshihiro and Iwamura, Masakazu and Akiba, Takuya and Kise, Koichi},
  journal=iclrwork,
  year={2018}
}
@article{zhang2018graph,
  title={Graph hypernetworks for neural architecture search},
  author={Zhang, Chris and Ren, Mengye and Urtasun, Raquel},
  journal=iclr,
  year={2018}
}
@article{xie2018snas,
  title={SNAS: stochastic neural architecture search},
  author={Xie, Sirui and Zheng, Hehui and Liu, Chunxiao and Lin, Liang},
  journal={arXiv preprint arXiv:1812.09926},
  year={2018}
}
@article{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  journal=cvpr,
  year={2016}
}
@article{szegedy2015going,
  title={Going deeper with convolutions},
  author={Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, Andrew},
  journal=cvpr,
  year={2015}
}
@article{gadat2017optimal,
  title={Optimal non-asymptotic bound of the Ruppert-Polyak averaging without strong convexity},
  author={Gadat, S. and Panloup, F.},
  journal={arXiv preprint arXiv:1709.03342},
  year={2017}
}

@article{brock2017smash,
  title={SMASH: one-shot model architecture search through hypernetworks},
  author={Brock, Andrew and Lim, Theodore and Ritchie, James M and Weston, Nick},
  journal=iclr,
  year={2018}
}
@article{baker2017accelerating,
  title={Accelerating neural architecture search using performance prediction},
  author={Baker, Bowen and Gupta, Otkrist and Raskar, Ramesh and Naik, Nikhil},
  journal=iclrwork,
  year={2018}
}
@article{liu2018progressive,
  title={Progressive neural architecture search},
  author={Liu, Chenxi and Zoph, Barret and Neumann, Maxim and Shlens, Jonathon and Hua, Wei and Li, Li-Jia and Fei-Fei, Li and Yuille, Alan and Huang, Jonathan and Murphy, Kevin},
  journal=eccv,
  year={2018}
}
@article{cai2018efficient,
  title={Efficient architecture search by network transformation},
  author={Cai, Han and Chen, Tianyao and Zhang, Weinan and Yu, Yong and Wang, Jun},
  journal=AAAI,
  year={2018}
}
@article{negrinho2017deeparchitect,
  title={Deeparchitect: Automatically designing and training deep architectures},
  author={Negrinho, Renato and Gordon, Geoff},
  journal={arXiv preprint arXiv:1704.08792},
  year={2017}
}
@article{elsken2018efficient,
  title={Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution},
  author={Elsken, Thomas and Metzen, Jan Hendrik and Hutter, Frank},
  journal=iclr,
  year={2019}
}
@article{jin2018efficient,
  title={Efficient neural architecture search with network morphism},
  author={Jin, Haifeng and Song, Qingquan and Hu, Xia},
  journal={arXiv preprint arXiv:1806.10282},
  year={2018}
}
@article{liu2017hierarchical,
  title={Hierarchical representations for efficient architecture search},
  author={Liu, Hanxiao and Simonyan, Karen and Vinyals, Oriol and Fernando, Chrisantha and Kavukcuoglu, Koray},
  journal={arXiv preprint arXiv:1711.00436},
  year={2017}
}
@article{xie2019exploring,
  title={Exploring Randomly Wired Neural Networks for Image Recognition},
  author={Xie, Saining and Kirillov, Alexander and Girshick, Ross and He, Kaiming},
  journal={arXiv preprint arXiv:1904.01569},
  year={2019}
}
@article{kandasamy2018neural,
  title={Neural architecture search with bayesian optimisation and optimal transport},
  author={Kandasamy, Kirthevasan and Neiswanger, Willie and Schneider, Jeff and Poczos, Barnabas and Xing, Eric P},
  journal = neurips,
  year={2018}
}
@article{elsken2018neural,
  title={Neural architecture search: A survey},
  author={Elsken, Thomas and Metzen, Jan Hendrik and Hutter, Frank},
  journal={arXiv preprint arXiv:1808.05377},
  year={2018}
}
@article{luo2018neural,
  title={Neural architecture optimization},
  author={Luo, Renqian and Tian, Fei and Qin, Tao and Chen, Enhong and Liu, Tie-Yan},
  journal=neurips,
  year={2018}
}
@article{sciuto2019evaluating,
  title={Evaluating the Search Phase of Neural Architecture Search},
  author={Sciuto, Christian and Yu, Kaicheng and Jaggi, Martin and Musat, Claudiu and Salzmann, Mathieu},
  journal={arXiv preprint arXiv:1902.08142},
  year={2019}
}
@article{tan2018mnasnet,
  title={Mnasnet: Platform-aware neural architecture search for mobile},
  author={Tan, Mingxing and Chen, Bo and Pang, Ruoming and Vasudevan, Vijay and Le, Quoc V},
  journal={arXiv preprint arXiv:1807.11626},
  year={2018}
}
@article{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q},
  journal=cvpr,
  year={2017}
}
@article{rauhut2010compressive,
  title={Compressive sensing and structured random matrices},
  author={Rauhut, Holger},
  journal={Theoretical foundations and numerical methods for sparse recovery},
  volume={9},
  pages={1--92},
  year={2010}
}
@incollection{miikkulainen2019evolving,
  title={Evolving deep neural networks},
  author={Miikkulainen, Risto and Liang, Jason and Meyerson, Elliot and Rawal, Aditya and Fink, Daniel and Francon, Olivier and Raju, Bala and Shahrzad, Hormoz and Navruzyan, Arshak and Duffy, Nigel and others},
  booktitle={Artificial Intelligence in the Age of Neural Networks and Brain Computing},
  pages={293--312},
  year={2019},
  publisher={Elsevier}
}
@article{donoho2006compressed,
  title={Compressed sensing},
  author={Donoho, David L and others},
  journal=tit,
  year={2006}
}
@article{candes2004robust,
  title={Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information},
  author={Candes, Emmanuel and Romberg, Justin and Tao, Terence},
  journal=tit,
  year={2006}
}
@article{cao2019learnable,
  title={Learnable embedding space for efficient neural architecture compression},
  author={Cao, Shengcao and Wang, Xiaofang and Kitani, Kris M},
  journal=iclr,
  year={2019}
}
@article{jaderberg2017population,
  title={Population based training of neural networks},
  author={Jaderberg, Max and Dalibard, Valentin and Osindero, Simon and Czarnecki, Wojciech M and Donahue, Jeff and Razavi, Ali and Vinyals, Oriol and Green, Tim and Dunning, Iain and Simonyan, Karen and others},
  journal={arXiv preprint arXiv:1711.09846},
  year={2017}
}
@article{candes2006near,
 title={Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?},
 author={Candes, Emmanuel J and Tao, Terence},
 journal=tit,
 year={2006},
}

@article{rudelson2008sparse,
 title={On sparse reconstruction from Fourier and Gaussian measurements},
 author={Rudelson, Mark and Vershynin, Roman},
 journal={Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences},
 year={2008}
}
@article{cheraghchi2013restricted,
 title={Restricted isometry of Fourier matrices and list decodability of random linear codes},
 author={Cheraghchi, Mahdi and Guruswami, Venkatesan and Velingker, Ameya},
 journal={SIAM Journal on Computing},
 year={2013},
}
@article{foucart2017mathematical,
  title={A mathematical introduction to compressive sensing},
  author={Foucart, Simon and Rauhut, Holger},
  journal={Bull. Am. Math},
  volume={54},
  pages={151--165},
  year={2017}
}
@article{bourgain2014improved,
 title={An Improved Estimate in the Restricted Isometry Problem},
 author={Bourgain, Jean},
 journal={Geometric Aspects of Functional Analysis: Israel Seminar (GAFA)},
 pages={65},
 year={2014},
}
@article{haviv2017restricted,
 title={The restricted isometry property of subsampled Fourier matrices},
 author={Haviv, Ishay and Regev, Oded},
 journal={Geometric Aspects of Functional Analysis},
 pages={163--179},
 year={2017},
 publisher={Springer}
}
@article{blumensath2009iterative,
 title={Iterative hard thresholding for compressed sensing},
 author={Blumensath, Thomas and Davies, Mike E},
 journal={Applied and computational harmonic analysis},
 year={2009},
}
@article{tropp2007signal,
 title={Signal recovery from random measurements via orthogonal matching pursuit},
 author={Tropp, Joel A and Gilbert, Anna C},
 journal=tit,
 year={2007}
}

@incollection{Bengio+chapter2007,
author = {Bengio, Yoshua and LeCun, Yann},
booktitle = {Large Scale Kernel Machines},
publisher = {MIT Press},
title = {Scaling Learning Algorithms Towards {AI}},
year = {2007}
}

@article{Hinton06,
author = {Hinton, Geoffrey E. and Osindero, Simon and Teh, Yee Whye},
journal = {Neural Computation},
pages = {1527--1554},
title = {A Fast Learning Algorithm for Deep Belief Nets},
volume = {18},
year = {2006}
}

@book{goodfellow2016deep,
title={Deep learning},
author={Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron and Bengio, Yoshua},
volume={1},
year={2016},
publisher={MIT Press}
}

@article{candes2008restricted,
  title={The restricted isometry property and its implications for compressed sensing},
  author={Candes, Emmanuel J},
  journal={Comptes rendus mathematique},
  volume={346},
  number={9-10},
  pages={589--592},
  year={2008},
  publisher={Elsevier}
}

@article{cho2019one,
  title={One-shot neural architecture search via compressive sensing},
  author={Cho, Minsu and Soltani, Mohammadreza and Hegde, Chinmay},
  journal={arXiv preprint arXiv:1906.02869},
  year={2019}
}

@article{lecun2010mnist,
  title={MNIST handwritten digit database},
  author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
  journal={AT\&T Labs [Online]. Available: http://yann. lecun. com/exdb/mnist},
  volume={2},
  pages={18},
  year={2010}
}

@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 :1708.07747},
  year={2017}
}

@article{marcus1993building,
  title={Building a large annotated corpus of English: The Penn Treebank},
  author={Marcus, Mitchell and Santorini, Beatrice and Marcinkiewicz, Mary Ann},
  year={1993}
}

@article{jongejan2016quickdraw,
  title={The Quick, Draw! - A.I. Experiment},
  author={J., Jongejan and H., Rowley and T., Kawashima and J., Kim and N.,Fox-Gieg},
  journal={https://quickdraw.withgoogle.com/},
  year={2016}
}

@article{mellor2020neural,
  title={Neural Architecture Search without Training},
  author={Mellor, Joseph and Turner, Jack and Storkey, Amos and Crowley, Elliot J},
  journal={arXiv preprint arXiv:2006.04647},
  year={2020}
}

@article{dong2020bench,
  title={Nas-bench-102: Extending the scope of reproducible neural architecture search},
  author={Dong, Xuanyi and Yang, Yi},
  journal={arXiv preprint arXiv:2001.00326},
  year={2020}
}

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

@article{simonyan2014very,
  title={Very deep convolutional networks for large-scale image recognition},
  author={Simonyan, Karen and Zisserman, Andrew},
  journal={arXiv preprint arXiv:1409.1556},
  year={2014}
}

@article{Polyak1992AccelerationOS,
  title={Acceleration of stochastic approximation by averaging},
  author={B. Polyak and A. Juditsky},
  journal={Siam Journal on Control and Optimization},
  year={1992},
  volume={30},
  pages={838-855}
}

@inproceedings{zamir2018taskonomy,
  title={Taskonomy: Disentangling Task Transfer Learning},
  author={Zamir, Amir R and Sax, Alexander and Shen, William B and Guibas, Leonidas and Malik, Jitendra and Savarese, Silvio},
  booktitle={2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2018},
  organization={IEEE}
}

@article{ILSVRC15,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = {{ImageNet Large Scale Visual Recognition Challenge}},
Year = {2015},
journal   = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}



@inproceedings{taskonomy2018,
title={Taskonomy: Disentangling Task Transfer Learning},
author={Amir R. Zamir and Alexander Sax and William B. Shen and Leonidas J. Guibas and Jitendra Malik and Silvio Savarese},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2018},
organization={IEEE},
}

@article{baker2017designing,
  title={Designing Neural Network Architectures using Reinforcement Learning},
  author={Baker, Bowen and Gupta, Otkrist and Naik, Nikhil and Raskar, Ramesh},
  journal={International Conference on Learning Representations},
  year={2017}
}

@article{sun2019adashare,
  title={AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning},
  author={Sun, Ximeng and Panda, Rameswar and Feris, Rogerio},
  journal={arXiv preprint arXiv:1911.12423},
  year={2019}
}

@inproceedings{liu2019end,
  title={End-To-End Multi-Task Learning With Attention},
  author={Liu, Shikun and Johns, Edward and Davison, Andrew J},
  booktitle={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={1871--1880},
  year={2019},
  organization={IEEE}
}

@misc{suteu2019regularizing,
    title={Regularizing Deep Multi-Task Networks using Orthogonal Gradients},
    author={Mihai Suteu and Yike Guo},
    year={2019},
    eprint={1912.06844},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

@InProceedings{Chennupati_2019_CVPR_Workshops,
author = {Chennupati, Sumanth and Sistu, Ganesh and Yogamani, Senthil and Rawashdeh, Samir},
title = {MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}

@article{leang2020dynamic,
  title={Dynamic Task Weighting Methods for Multi-task Networks in Autonomous Driving Systems},
  author={Leang, Isabelle and Sistu, Ganesh and Burger, Fabian and Bursuc, Andrei and Yogamani, Senthil},
  journal={arXiv preprint arXiv:2001.02223},
  year={2020}
}

@ARTICLE{8848395,
author={T. {Gong} and T. {Lee} and C. {Stephenson} and V. {Renduchintala} and S. {Padhy} and A. {Ndirango} and G. {Keskin} and O. H. {Elibol}},
journal={IEEE Access},
title={A Comparison of Loss Weighting Strategies for Multi task Learning in Deep Neural Networks},
year={2019},
volume={7},
number={},
pages={141627-141632},
keywords={learning (artificial intelligence);neural nets;deep neural networks;deep multitask learning models;task pairs;task specific branches architecture;task-specific cost functions;loss weighting strategies;dynamic weight average;Task analysis;Training;Uncertainty;Deep learning;Data models;Estimation;Training data;Dynamic weighting average;multi-MNIST;multi-objective optimization;multi-task learning;uncertainty weighting},
doi={10.1109/ACCESS.2019.2943604},
ISSN={2169-3536},
month={},}


@inproceedings{47354,
  author    = {Esteban Real and
               Alok Aggarwal and
               Yanping Huang and
               Quoc V. Le},
  title     = {Regularized Evolution for Image Classifier Architecture Search},
  booktitle = {The Thirty-Third {AAAI} Conference on Artificial Intelligence, {AAAI}
               2019, The Thirty-First Innovative Applications of Artificial Intelligence
               Conference, {IAAI} 2019, The Ninth {AAAI} Symposium on Educational
               Advances in Artificial Intelligence, {EAAI} 2019, Honolulu, Hawaii,
               USA, January 27 - February 1, 2019},
  pages     = {4780--4789},
  publisher = {{AAAI} Press},
  year      = {2019},
  url       = {https://doi.org/10.1609/aaai.v33i01.33014780},
  doi       = {10.1609/aaai.v33i01.33014780},
  timestamp = {Wed, 25 Sep 2019 11:05:08 +0200},
  biburl    = {https://dblp.org/rec/conf/aaai/RealAHL19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}






@InProceedings{pmlr-v97-zhou19e,
  title = 	 {{B}ayes{NAS}: A {B}ayesian Approach for Neural Architecture Search},
  author = 	 {Zhou, Hongpeng and Yang, Minghao and Wang, Jun and Pan, Wei},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {7603--7613},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Long Beach, California, USA},
  month = 	 {09--15 Jun},
  publisher = 	 {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/zhou19e/zhou19e.pdf},
  url = 	 {http://proceedings.mlr.press/v97/zhou19e.html},
  abstract = 	 {One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training. It can be treated as a Network Compression problem on the architecture parameters from an over-parameterized network. However, there are two issues associated with most one-shot NAS methods. First, dependencies between a node and its predecessors and successors are often disregarded which result in improper treatment over zero operations. Second, architecture parameters pruning based on their magnitude is questionable. In this paper, we employ the classic Bayesian learning approach to alleviate these two issues by modeling architecture parameters using hierarchical automatic relevance determination (HARD) priors. Unlike other NAS methods, we train the over-parameterized network for only one epoch then update the architecture. Impressively, this enabled us to find the architecture in both proxy and proxyless tasks on CIFAR-10 within only 0.2 GPU days using a single GPU. As a byproduct, our approach can be transferred directly to compress convolutional neural networks by enforcing structural sparsity which achieves extremely sparse networks without accuracy deterioration.}
}

@inproceedings{45826,
  author    = {Barret Zoph and
               Quoc V. Le},
  title     = {Neural Architecture Search with Reinforcement Learning},
  booktitle = {5th International Conference on Learning Representations, {ICLR} 2017,
               Toulon, France, April 24-26, 2017, Conference Track Proceedings},
  publisher = {OpenReview.net},
  year      = {2017},
  url       = {https://openreview.net/forum?id=r1Ue8Hcxg},
  timestamp = {Thu, 04 Apr 2019 13:20:08 +0200},
  biburl    = {https://dblp.org/rec/conf/iclr/ZophL17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@InProceedings{46637,
  title = 	 {Efficient Neural Architecture Search via Parameters Sharing},
  author = 	 {Pham, Hieu and Guan, Melody and Zoph, Barret and Le, Quoc and Dean, Jeff},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {4095--4104},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Stockholmsmässan, Stockholm Sweden},
  month = 	 {10--15 Jul},
  publisher = 	 {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v80/pham18a/pham18a.pdf},
  url = 	 {http://proceedings.mlr.press/v80/pham18a.html},
  abstract = 	 {We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. ENAS constructs a large computational graph, where each subgraph represents a neural network architecture, hence forcing all architectures to share their parameters. A controller is trained with policy gradient to search for a subgraph that maximizes the expected reward on a validation set. Meanwhile a model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Sharing parameters among child models allows ENAS to deliver strong empirical performances, whilst using much fewer GPU-hours than existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On Penn Treebank, ENAS discovers a novel architecture that achieves a test perplexity of 56.3, on par with the existing state-of-the-art among all methods without post-training processing. On CIFAR-10, ENAS finds a novel architecture that achieves 2.89% test error, which is on par with the 2.65% test error of NASNet (Zoph et al., 2018).}
}

@article{DBLP:journals/corr/RusuRDSKKPH16,
  title={Progressive neural networks},
  author={Rusu, Andrei A and Rabinowitz, Neil C and Desjardins, Guillaume and Soyer, Hubert and Kirkpatrick, James and Kavukcuoglu, Koray and Pascanu, Razvan and Hadsell, Raia},
  journal={arXiv preprint arXiv:1606.04671},
  year={2016}
}

@inproceedings{bengio2009curriculum,
 title={Curriculum learning},
 author={Bengio, Yoshua and Louradour, J{\’e}r{\^o}me and Collobert, Ronan and Weston, Jason},
 booktitle={Proceedings of the 26th annual international conference on machine learning},
 pages={41--48},
 year={2009},
 organization={ACM}
}

@INPROCEEDINGS{7780459,
author={K. {He} and X. {Zhang} and S. {Ren} and J. {Sun}},
booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title={Deep Residual Learning for Image Recognition},
year={2016},
volume={},
number={},
pages={770-778},
keywords={image classification;learning (artificial intelligence);neural nets;object detection;COCO segmentation;ImageNet localization;ILSVRC & COCO 2015 competitions;deep residual nets;COCO object detection dataset;visual recognition tasks;CIFAR-10;ILSVRC 2015 classification task;ImageNet test set;VGG nets;residual nets;ImageNet dataset;residual function learning;deeper neural network training;image recognition;deep residual learning;Training;Degradation;Complexity theory;Image recognition;Neural networks;Visualization;Image segmentation},
doi={10.1109/CVPR.2016.90},
ISSN={},
month={June},}

@article{wang2019neural,
  title={Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity},
  author={Wang, Aria Y and Wehbe, Leila and Tarr, Michael J},
  journal={BioRxiv},
  pages={708016},
  year={2019},
  publisher={Cold Spring Harbor Laboratory}
}

@inproceedings{bingel-sogaard-2017-identifying,
    title = "Identifying beneficial task relations for multi-task learning in deep neural networks",
    author = "Bingel, Joachim  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/E17-2026",
    pages = "164--169",
    abstract = "Multi-task learning (MTL) in deep neural networks for NLP has recently receivedincreasing interest due to some compelling benefits, including its potential toefficiently regularize models and to reduce the need for labeled data. While ithas brought significant improvements in a number of NLP tasks, mixed resultshave been reported, and little is known about the conditions under which MTLleads to gains in NLP. This paper sheds light on the specific task relationsthat can lead to gains from MTL models over single-task setups.",
}

@INPROCEEDINGS{Thrun96islearning,
    author = {Sebastian Thrun},
    title = {Is Learning The n-th Thing Any Easier Than Learning The First?},
    booktitle = {Advances in Neural Information Processing Systems},
    year = {1996},
    pages = {640--646},
    publisher = {The MIT Press}
}

@Article{Caruana1997,
author="Caruana, Rich",
title="Multitask Learning",
journal="Machine Learning",
year="1997",
month="Jul",
day="01",
volume="28",
number="1",
pages="41--75",
issn="1573-0565",
doi="10.1023/A:1007379606734",
url="https://doi.org/10.1023/A:1007379606734"
}


@inproceedings{10.5555/2886521.2886688,
author = {Han, Lei and Zhang, Yu},
title = {Learning Multi-Level Task Groups in Multi-Task Learning},
year = {2015},
isbn = {0262511290},
publisher = {AAAI Press},
booktitle = {Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence},
pages = {2638–2644},
numpages = {7},
location = {Austin, Texas},
series = {AAAI’15}
}

@inproceedings{DBLP:conf/cvpr/ManinisRK19,
  author    = {Kevis{-}Kokitsi Maninis and
               Ilija Radosavovic and
               Iasonas Kokkinos},
  title     = {Attentive Single-Tasking of Multiple Tasks},
  booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}
               2019, Long Beach, CA, USA, June 16-20, 2019},
  pages     = {1851--1860},
  publisher = {Computer Vision Foundation / {IEEE}},
  year      = {2019},
  url       = {http://openaccess.thecvf.com/content\_CVPR\_2019/html/Maninis\_Attentive\_Single-Tasking\_of\_Multiple\_Tasks\_CVPR\_2019\_paper.html},
  doi       = {10.1109/CVPR.2019.00195},
  timestamp = {Mon, 20 Jan 2020 15:36:04 +0100},
  biburl    = {https://dblp.org/rec/conf/cvpr/ManinisRK19.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@paper{AAAI1714430,
	author = {Yu Zhang and Qiang Yang},
	title = {Learning Sparse Task Relations in Multi-Task Learning},
	conference = {AAAI Conference on Artificial Intelligence},
	year = {2017},
	keywords = {Multi-Task Learning},
	abstract = {In multi-task learning, when the number of tasks is large, pairwise task relations exhibit sparse patterns since usually a task cannot be helpful to all of the other tasks and moreover, sparse task relations can reduce the risk of overfitting compared with the dense ones. In this paper, we focus on learning sparse task relations. Based on a regularization framework which can learn task relations among multiple tasks, we propose a SParse covAriance based mulTi-taSk (SPATS) model to learn a sparse covariance by using the ℓl regularization. The resulting objective function of the SPATS method is convex, which allows us to devise an alternating method to solve it. Moreover, some theoretical properties of the proposed model are studied. Experiments on synthetic and real-world datasets demonstrate the effectiveness of the proposed method.},

	url = {https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14430}
}



@inproceedings{DBLP:conf/cvpr/ZophVSL18,
  author    = {Barret Zoph and
               Vijay Vasudevan and
               Jonathon Shlens and
               Quoc V. Le},
  title     = {Learning Transferable Architectures for Scalable Image Recognition},
  booktitle = {2018 {IEEE} Conference on Computer Vision and Pattern Recognition,
               {CVPR} 2018, Salt Lake City, UT, USA, June 18-22, 2018},
  pages     = {8697--8710},
  publisher = {{IEEE} Computer Society},
  year      = {2018},
  url       = {http://openaccess.thecvf.com/content\_cvpr\_2018/html/Zoph\_Learning\_Transferable\_Architectures\_CVPR\_2018\_paper.html},
  doi       = {10.1109/CVPR.2018.00907},
  timestamp = {Wed, 16 Oct 2019 14:14:50 +0200},
  biburl    = {https://dblp.org/rec/conf/cvpr/ZophVSL18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@ARTICLE{8123910, 
author={D. {Zhou} and J. {Wang} and B. {Jiang} and H. {Guo} and Y. {Li}}, 
journal={IEEE Access}, 
title={Multi-Task Multi-View Learning Based on Cooperative Multi-Objective Optimization}, 
year={2018}, 
volume={6}, 
number={}, 
pages={19465-19477}, 
keywords={learning (artificial intelligence);mathematics computing;Pareto optimisation;particle swarm optimisation;quantum computing;multitask multiview learning;single-objective learning framework;multiobjective MTMV learning method;MTMV problem;multiobjective optimization problem;CMO-MTMV method;particle swarm optimization algorithm;multiswarm scheme;numerical optimization methods;view-view relations;multitask multiview models;single-objective MTMV learning methods;instance-instance relations;feature-feature relations;task-task relations;cooperative multiobjective quantum;cooperative multiobjective quantum-behaved particle swarm optimization;CMOQPSO;Optimization;Convergence;Particle swarm optimization;Learning systems;Linear programming;Sun;Multi-task multi-view learning;multi-objective optimization;quantum-behaved particle swarm optimization;multi-swarm strategy}, 
doi={10.1109/ACCESS.2017.2777888}, 
ISSN={2169-3536}, 
month={},}

@INPROCEEDINGS{6378235, 
author={P. B. C. d. {Miranda} and R. B. C. {Prudêncio} and A. C. P. L. F. d. {Carvalho} and C. {Soares}}, 
booktitle={2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, 
title={Combining a multi-objective optimization approach with meta-learning for SVM parameter selection}, 
year={2012}, 
volume={}, 
number={}, 
pages={2909-2914}, 
keywords={learning (artificial intelligence);Pareto optimisation;particle swarm optimisation;pattern classification;support vector machines;multiobjective optimization approach;metalearning;SVM parameter selection;support vector machines;supervised learning technique;particle swarm optimization;crowding distance mechanism;classification problem;random initialization;Pareto front;Support vector machines;Measurement;Search problems;Optimization;Sociology;Statistics;Proposals;Meta-Learning;Multi-Objective Optimization;SVM Parameter Selection;Particle Swarm Optimization}, 
doi={10.1109/ICSMC.2012.6378235}, 
ISSN={1062-922X}, 
month={Oct},}

@Article{RePEc:eee:ejores:v:271:y:2018:i:3:p:808-817,
  author={Mercier, Quentin and Poirion, Fabrice and Désidéri, Jean-Antoine},
  title={{A stochastic multiple gradient descent algorithm}},
  journal={European Journal of Operational Research},
  year=2018,
  volume={271},
  number={3},
  pages={808-817},
  month={},
  keywords={Multiple objective programming; Multiobjective stochastic optimization; Stochastic gradient algorith},
  doi={10.1016/j.ejor.2018.05.06},
  abstract={In this article, we propose a new method for multiobjective optimization problems in which the objective functions are expressed as expectations of random functions. The present method is based on an extension of the classical stochastic gradient algorithm and a deterministic multiobjective algorithm, the Multiple Gradient Descent Algorithm (MGDA). In MGDA a descent direction common to all specified objective functions is identified through a result of convex geometry. The use of this common descent vector and the Pareto stationarity definition into the stochastic gradient algorithm makes the algorithm able to solve multiobjective problems. The mean square and almost sure convergence of this new algorithm are proven considering the classical stochastic gradient algorithm hypothesis. The algorithm efficiency is illustrated on a set of benchmarks with diverse complexity and assessed in comparison with two classical algorithms (NSGA-II, DMS) coupled with a Monte Carlo expectation estimator.},
  url={https://ideas.repec.org/a/eee/ejores/v271y2018i3p808-817.html}
}

@incollection{NIPS2017_6757,
title = {Learning Multiple Tasks with Multilinear Relationship Networks},
author = {Long, Mingsheng and CAO, ZHANGJIE and Wang, Jianmin and Yu, Philip S},
booktitle = {Advances in Neural Information Processing Systems 30},
editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {1594--1603},
year = {2017},
publisher = {Curran Associates, Inc.},
_url = {http://papers.nips.cc/paper/6757-learning-multiple-tasks-with-multilinear-relationship-networks.pdf}
}

@article{DBLP:journals/corr/ZhangY17aa,
  author    = {Yu Zhang and
               Qiang Yang},
  title     = {A Survey on Multi-Task Learning},
  journal   = {CoRR},
  volume    = {abs/1707.08114},
  year      = {2017},
  url       = {http://arxiv.org/abs/1707.08114},
  archivePrefix = {arXiv},
  eprint    = {1707.08114},
  timestamp = {Mon, 13 Aug 2018 16:49:00 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/ZhangY17aa},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@ARTICLE{5288526, 
author={S. J. {Pan} and Q. {Yang}}, 
journal={IEEE Transactions on Knowledge and Data Engineering}, 
title={A Survey on Transfer Learning}, 
year={2010}, 
volume={22}, 
number={10}, 
pages={1345-1359}, 
keywords={knowledge engineering;learning by example;optimisation;unsupervised learning;machine learning;data mining;knowledge transfer;inductive transfer learning;transductive transfer learning;unsupervised transfer learning;Machine learning;Training data;Data mining;Knowledge transfer;Space technology;Knowledge engineering;Machine learning algorithms;Labeling;Learning systems;Testing;Transfer learning;survey;machine learning;data mining.}, 
doi={10.1109/TKDE.2009.191}, 
ISSN={1041-4347}, 
month={Oct},}

@inproceedings{Razavian:2014:CFO:2679599.2679731,
  title={CNN features off-the-shelf: an astounding baseline for recognition},
  author={Sharif Razavian, Ali and Azizpour, Hossein and Sullivan, Josephine and Carlsson, Stefan},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
  pages={806--813},
  year={2014}
}

@inproceedings{achille2019task2vec,
  title={Task2vec: Task embedding for meta-learning},
  author={Achille, Alessandro and Lam, Michael and Tewari, Rahul and Ravichandran, Avinash and Maji, Subhransu and Fowlkes, Charless C and Soatto, Stefano and Perona, Pietro},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={6430--6439},
  year={2019}
}

@inproceedings{pal2019zeroshot,
  title={Zero-shot task transfer},
  author={Pal, Arghya and Balasubramanian, Vineeth N},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2189--2198},
  year={2019}
}


@inproceedings{dwivedi2019,
  title={Representation similarity analysis for efficient task taxonomy \& transfer learning},
  author={Dwivedi, Kshitij and Roig, Gemma},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12387--12396},
  year={2019}
}


@inproceedings{DBLP:conf/cvpr/LuKZCJF17,
  author    = {Yongxi Lu and
               Abhishek Kumar and
               Shuangfei Zhai and
               Yu Cheng and
               Tara Javidi and
               Rog{\'{e}}rio Schmidt Feris},
  title     = {Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications
               in Person Attribute Classification},
  booktitle = {{CVPR}},
  pages     = {1131--1140},
  publisher = {{IEEE} Computer Society},
  year      = {2017}
}

@inproceedings{luolabel,
	title={Label Efficient Learning of Transferable Representations acrosss Domains and Tasks},
	author={Luo, Zelun and Zou, Yuliang and Hoffman, Judy and Fei-Fei, Li F},
	booktitle={Advances in Neural Information Processing Systems},
	pages={164--176},
	year={2017}
}

@inproceedings{niculescu2007inductive,
	title={Inductive transfer for Bayesian network structure learning},
	author={Niculescu-Mizil, Alexandru and Caruana, Rich},
	booktitle={Artificial Intelligence and Statistics},
	pages={339--346},
	year={2007}
}

@inproceedings{mihalkova2007mapping,
	title={Mapping and revising Markov logic networks for transfer learning},
	author={Mihalkova, Lilyana and Huynh, Tuyen and Mooney, Raymond J},
	booktitle={AAAI},
	volume={7},
	pages={608--614},
	year={2007}
}

@inproceedings{finn2016deep,
  title={Deep spatial autoencoders for visuomotor learning},
  author={Finn, Chelsea and Tan, Xin Yu and Duan, Yan and Darrell, Trevor and Levine, Sergey and Abbeel, Pieter},
  booktitle={2016 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={512--519},
  year={2016},
  organization={IEEE}
}

@InProceedings{10.1007/978-3-642-04180-8_52,
author="Helleputte, Thibault
and Dupont, Pierre",
editor="Buntine, Wray
and Grobelnik, Marko
and Mladeni{\'{c}}, Dunja
and Shawe-Taylor, John",
title="Feature Selection by Transfer Learning with Linear Regularized Models",
booktitle="Machine Learning and Knowledge Discovery in Databases",
year="2009",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="533--547",
abstract="This paper presents a novel feature selection method for classification of high dimensional data, such as those produced by microarrays. It includes a partial supervision to smoothly favor the selection of some dimensions (genes) on a new dataset to be classified. The dimensions to be favored are previously selected from similar datasets in large microarray databases, hence performing inductive transfer learning at the feature level. This technique relies on a feature selection method embedded within a regularized linear model estimation. A practical approximation of this technique reduces to linear SVM learning with iterative input rescaling. The scaling factors depend on the selected dimensions from the related datasets. The final selection may depart from those whenever necessary to optimize the classification objective. Experiments on several microarray datasets show that the proposed method both improves the selected gene lists stability, with respect to sampling variation, as well as the classification performances.",
isbn="978-3-642-04180-8"
}

@InProceedings{Zhang_2019_CVPR,
author = {Zhang, Zhenyu and Cui, Zhen and Xu, Chunyan and Yan, Yan and Sebe, Nicu and Yang, Jian},
title = {Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

@article{silver2008guest,
	title={Guest editor’s introduction: special issue on inductive transfer learning},
	author={Silver, Daniel L and Bennett, Kristin P},
	journal={Machine Learning},
	volume={73},
	number={3},
	pages={215--220},
	year={2008},
	publisher={Springer}
}


@inproceedings{DBLP:conf/cvpr/Kokkinos17,
  author    = {Iasonas Kokkinos},
  title     = {UberNet: Training a Universal Convolutional Neural Network for Low-,
               Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory},
  booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition,
               {CVPR} 2017, Honolulu, HI, USA, July 21-26, 2017},
  pages     = {5454--5463},
  year      = {2017},
  url       = {https://doi.org/10.1109/CVPR.2017.579},
  doi       = {10.1109/CVPR.2017.579},
  timestamp = {Tue, 14 Nov 2017 17:15:06 +0100},
  biburl    = {https://dblp.org/rec/bib/conf/cvpr/Kokkinos17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@article{zhang2021understanding,
  title={Understanding deep learning (still) requires rethinking generalization},
  author={Zhang, Chiyuan and Bengio, Samy and Hardt, Moritz and Recht, Benjamin and Vinyals, Oriol},
  journal={Communications of the ACM},
  volume={64},
  number={3},
  pages={107--115},
  year={2021},
  publisher={ACM New York, NY, USA}
}

@article{DBLP:journals/corr/Ruder17a,
  author    = {Sebastian Ruder},
  title     = {An Overview of Multi-Task Learning in Deep Neural Networks},
  journal   = {CoRR},
  volume    = {abs/1706.05098},
  year      = {2017},
  url       = {http://arxiv.org/abs/1706.05098},
  archivePrefix = {arXiv},
  eprint    = {1706.05098},
  timestamp = {Mon, 03 Jul 2017 13:29:02 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/Ruder17a},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Tessler:2017:DHA:3298239.3298465,
 author = {Tessler, Chen and Givony, Shahar and Zahavy, Tom and Mankowitz, Daniel J. and Mannor, Shie},
 title = {A Deep Hierarchical Approach to Lifelong Learning in Minecraft},
 booktitle = {Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence},
 series = {AAAI'17},
 year = {2017},
 location = {San Francisco, California, USA},
 pages = {1553--1561},
 numpages = {9},
 url = {http://dl.acm.org.stanford.idm.oclc.org/citation.cfm?id=3298239.3298465},
 acmid = {3298465},
 publisher = {AAAI Press},
} 

@article{Dai2016InstanceAwareSS,
  title={Instance-Aware Semantic Segmentation via Multi-task Network Cascades},
  author={Jifeng Dai and Kaiming He and Jian Sun},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={3150-3158}
}


@inproceedings{doersch2017multitask,
  title = {Multi-task Self-Supervised Visual Learning},
  author = {Doersch, Carl and Zisserman, Andrew},
  booktitle = {International Conference on Computer Vision},
  year = {2017},
}

@inproceedings{DBLP:conf/eccv/RuddGB16,
  author    = {Ethan M. Rudd and
               Manuel G{\"{u}}nther and
               Terrance E. Boult},
  title     = {{MOON:} {A} Mixed Objective Optimization Network for the Recognition
               of Facial Attributes},
  booktitle = {{ECCV} {(5)}},
  series    = {Lecture Notes in Computer Science},
  volume    = {9909},
  pages     = {19--35},
  publisher = {Springer},
  year      = {2016}
}

@article{kuhn1955hungarian,
  title={The Hungarian method for the assignment problem},
  author={Kuhn, Harold W},
  journal={Naval research logistics quarterly},
  volume={2},
  number={1-2},
  pages={83--97},
  year={1955},
  publisher={Wiley Online Library}
}

@incollection{NIPS1992_641,
title = {Discriminability-Based Transfer between Neural Networks},
author = {Pratt, L. Y.},
booktitle = {Advances in Neural Information Processing Systems 5},
editor = {S. J. Hanson and J. D. Cowan and C. L. Giles},
pages = {204--211},
year = {1993},
publisher = {Morgan-Kaufmann},
_url = {http://papers.nips.cc/paper/641-discriminability-based-transfer-between-neural-networks.pdf}
}

@article{zamir2020consistency,
  title={Robust Learning Through Cross-Task Consistency},
  author={Zamir, Amir and Sax, Alexander and Yeo, Teresa and Kar, Oğuzhan and Cheerla, Nikhil and Suri, Rohan and Cao, Zhangjie and Malik, Jitendra and Guibas, Leonidas},
  journal={arXiv},
  year={2020}
}

@inproceedings{36ebd0a9d3dd43f8bb0dbce896aa3c46,
  title     = "Trace Norm Regularised Deep Multi-Task Learning",
  abstract  = "We propose a framework for training multiple neural networks simultaneously. The parameters from all models are regularised by the tensor trace norm, so that each neural network is encouraged to reuse others' parameters if possible -- this is the main motivation behind multi-task learning. In contrast to many deep multi-task learning models, we do not predefine a parameter sharing strategy by specifying which layers have tied parameters. Instead, our framework considers sharing for all shareable layers, and the sharing strategy is learned in a data-driven way.",
  author    = "Yongxin Yang and Timothy Hospedales",
  year      = "2017",
  language  = "English",
  booktitle = "5th International Conference on Learning Representations Workshop",
}

@INPROCEEDINGS{7410535, 
author={H. {Noh} and S. {Hong} and B. {Han}}, 
booktitle={2015 IEEE International Conference on Computer Vision (ICCV)}, 
title={Learning Deconvolution Network for Semantic Segmentation}, 
year={2015}, 
volume={}, 
number={}, 
pages={1520-1528}, 
keywords={convolution;deconvolution;image segmentation;learning (artificial intelligence);neural nets;prediction theory;semantic networks;deconvolution network learning;semantic segmentation algorithm;convolutional neural network;CNN;proposal-wise prediction;Deconvolution;Semantics;Image segmentation;Visualization;Feature extraction;Shape;Image reconstruction}, 
doi={10.1109/ICCV.2015.178}, 
ISSN={2380-7504}, 
month={Dec},}

@InProceedings{Qi_2018_CVPR,
author = {Qi, Xiaojuan and Liao, Renjie and Liu, Zhengzhe and Urtasun, Raquel and Jia, Jiaya},
title = {GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}

@inproceedings{DBLP:conf/eccv/ChenZPSA18,
  author    = {Liang{-}Chieh Chen and
               Yukun Zhu and
               George Papandreou and
               Florian Schroff and
               Hartwig Adam},
  title     = {Encoder-Decoder with Atrous Separable Convolution for Semantic Image
               Segmentation},
  booktitle = {Computer Vision - {ECCV} 2018 - 15th European Conference, Munich,
               Germany, September 8-14, 2018, Proceedings, Part {VII}},
  pages     = {833--851},
  year      = {2018},
  url       = {https://doi.org/10.1007/978-3-030-01234-2\_49},
  doi       = {10.1007/978-3-030-01234-2\_49},
  timestamp = {Mon, 08 Oct 2018 17:08:05 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/eccv/ChenZPSA18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@InProceedings{10.1007/978-3-319-46487-9_33,
author="Zamir, Amir R.
and Wekel, Tilman
and Agrawal, Pulkit
and Wei, Colin
and Malik, Jitendra
and Savarese, Silvio",
editor="Leibe, Bastian
and Matas, Jiri
and Sebe, Nicu
and Welling, Max",
title="Generic 3D Representation via Pose Estimation and Matching",
booktitle="Computer Vision -- ECCV 2016",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="535--553",
abstract="Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been limited. In this paper, we learn a generic 3D representation through solving a set of foundational proxy 3D tasks: object-centric camera pose estimation and wide baseline feature matching. Our method is based upon the premise that by providing supervision over a set of carefully selected foundational tasks, generalization to novel tasks and abstraction capabilities can be achieved. We empirically show that the internal representation of a multi-task ConvNet trained to solve the above core problems generalizes to novel 3D tasks (e.g., scene layout estimation, object pose estimation, surface normal estimation) without the need for fine-tuning and shows traits of abstraction abilities (e.g., cross modality pose estimation).",
isbn="978-3-319-46487-9"
}

@incollection{NIPS2016_6502,
title = {SURGE: Surface Regularized Geometry Estimation from a Single Image},
author = {Wang, Peng and Shen, Xiaohui and Russell, Bryan and Cohen, Scott and Price, Brian and Yuille, Alan L},
booktitle = {Advances in Neural Information Processing Systems 29},
editor = {D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and R. Garnett},
pages = {172--180},
year = {2016},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/6502-surge-surface-regularized-geometry-estimation-from-a-single-image.pdf}
}

@InProceedings{pmlr-v70-pentina17a,
  title = 	 {Multi-task Learning with Labeled and Unlabeled Tasks},
  author = 	 {Anastasia Pentina and Christoph H. Lampert},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
  pages = 	 {2807--2816},
  year = 	 {2017},
  editor = 	 {Doina Precup and Yee Whye Teh},
  volume = 	 {70},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {International Convention Centre, Sydney, Australia},
  month = 	 {06--11 Aug},
  publisher = 	 {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v70/pentina17a/pentina17a.pdf},
  url = 	 {http://proceedings.mlr.press/v70/pentina17a.html},
  abstract = 	 {In multi-task learning, a learner is given a collection of prediction tasks and needs to solve all of them. In contrast to previous work, which required that annotated training data must be available for all tasks, we consider a new setting, in which for some tasks, potentially most of them, only unlabeled training data is provided. Consequently, to solve all tasks, information must be transferred between tasks with labels and tasks without labels. Focusing on an instance-based transfer method we analyze two variants of this setting: when the set of labeled tasks is fixed, and when it can be actively selected by the learner. We state and prove a generalization bound that covers both scenarios and derive from it an algorithm for making the choice of labeled tasks (in the active case) and for transferring information between the tasks in a principled way. We also illustrate the effectiveness of the algorithm on synthetic and real data.}
}


@inproceedings{DBLP:conf/icpr/RenYZH14,
  author    = {Weiqiang Ren and
               Yinan Yu and
               Junge Zhang and
               Kaiqi Huang},
  title     = {Learning Convolutional Nonlinear Features for {K} Nearest Neighbor
               Image Classification},
  booktitle = {22nd International Conference on Pattern Recognition, {ICPR} 2014,
               Stockholm, Sweden, August 24-28, 2014},
  pages     = {4358--4363},
  year      = {2014},
  crossref  = {DBLP:conf/icpr/2014},
  url       = {https://doi.org/10.1109/ICPR.2014.746},
  doi       = {10.1109/ICPR.2014.746},
  timestamp = {Wed, 24 May 2017 08:30:38 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/icpr/RenYZH14},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{imagenet,
  title={Imagenet: A large-scale hierarchical image database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={CVPR},
  pages={248--255},
  year={2009}
}

@article{DBLP:journals/corr/LinMBHPRDZ14,
  author    = {Tsung{-}Yi Lin and
               Michael Maire and
               Serge J. Belongie and
               Lubomir D. Bourdev and
               Ross B. Girshick and
               James Hays and
               Pietro Perona and
               Deva Ramanan and
               Piotr Doll{\'{a}}r and
               C. Lawrence Zitnick},
  title     = {Microsoft {COCO:} Common Objects in Context},
  journal   = {CoRR},
  volume    = {abs/1405.0312},
  year      = {2014}
}

@misc{pascal-voc-2012,
	author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.",
	title = "The {PASCAL} {V}isual {O}bject {C}lasses {C}hallenge 2012 {(VOC2012)} {R}esults",
	howpublished = "http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html"}
	
@InProceedings{BharathICCV2011,
author = "Bharath Hariharan and Pablo Arbelaez and Lubomir Bourdev and Subhransu Maji and Jitendra Malik",
title = "Semantic Contours from Inverse Detectors",
booktitle = "International Conference on Computer Vision (ICCV)",
year = "2011",
}

@ARTICLE{2017arXiv170201105A,
   author = {{Armeni}, I. and {Sax}, A. and {Zamir}, A.~R. and {Savarese}, S.
	},
    title = "{Joint 2D-3D-Semantic Data for Indoor Scene Understanding}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1702.01105},
 primaryClass = "cs.CV",
 keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics},
     year = 2017,
    month = feb,
   adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170201105A},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}


@article{Misra2016CrossStitchNF,
  title={Cross-Stitch Networks for Multi-task Learning},
  author={Ishan Misra and Abhinav Shrivastava and Abhinav Gupta and Martial Hebert},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={3994-4003}
}

@incollection{NIPS2016_6393,
title = {Integrated perception with recurrent multi-task neural networks},
author = {Bilen, Hakan and Vedaldi, Andrea},
booktitle = {Advances in Neural Information Processing Systems 29},
editor = {D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and R. Garnett},
pages = {235--243},
year = {2016},
publisher = {Curran Associates, Inc.},
_url = {http://papers.nips.cc/paper/6393-integrated-perception-with-recurrent-multi-task-neural-networks.pdf}
}

@InProceedings{duong-EtAl:2015:ACL-IJCNLP,
  author    = {Duong, Long  and  Cohn, Trevor  and  Bird, Steven  and  Cook, Paul},
  title     = {Low Resource Dependency Parsing: Cross-lingual Parameter Sharing in a Neural Network Parser},
  booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {July},
  year      = {2015},
  pages     = {845--850},
}


@inproceedings{Silberman:ECCV12,
  author    = {Nathan Silberman, Derek Hoiem, Pushmeet Kohli and Rob Fergus},
  title     = {Indoor Segmentation and Support Inference from RGBD Images},
  booktitle = {ECCV},
  year      = {2012}
}

@article{Chollet16a,
  author    = {Fran{\c{c}}ois Chollet},
  title     = {Xception: Deep Learning with Depthwise Separable Convolutions},
  journal   = {CoRR},
  volume    = {abs/1610.02357},
  year      = {2016},
  timestamp = {Wed, 07 Jun 2017 14:41:03 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/Chollet16a},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@InProceedings{RFB15a,
  author       = "O. Ronneberger and P.Fischer and T. Brox",
  title        = "U-Net: Convolutional Networks for Biomedical Image Segmentation",
  booktitle    = "Medical Image Computing and Computer-Assisted Intervention (MICCAI)",
  series       = "LNCS",
  volume       = "9351",
  pages        = "234--241",
  year         = "2015",
  publisher    = "Springer",
  note         = "(available on arXiv:1505.04597 [cs.CV])",
  url          = "http://lmb.informatik.uni-freiburg.de/Publications/2015/RFB15a"
}

@article{DBLP:journals/corr/abs-1809-04766,
  author    = {Vladimir Nekrasov and
               Thanuja Dharmasiri and
               Andrew Spek and
               Tom Drummond and
               Chunhua Shen and
               Ian D. Reid},
  title     = {Real-Time Joint Semantic Segmentation and Depth Estimation Using Asymmetric
               Annotations},
  journal   = {CoRR},
  volume    = {abs/1809.04766},
  year      = {2018},
  url       = {http://arxiv.org/abs/1809.04766},
  archivePrefix = {arXiv},
  eprint    = {1809.04766},
  timestamp = {Fri, 05 Oct 2018 11:34:52 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1809-04766},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@inproceedings{dvornik2017blitznet,
  title = {{BlitzNet}: A Real-Time Deep Network for Scene Understanding},
  author = {Dvornik, Nikita and Shmelkov, Konstantin and Mairal, Julien and Schmid, Cordelia},
  booktitle = {{IEEE International Conference on Computer Vision (ICCV)}},
  year = {2017}
}

@incollection{NeurIPS2018_Sener_Koltun,
title = {Multi-Task Learning as Multi-Objective Optimization},
author = {Sener, Ozan and Koltun, Vladlen},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {525--536},
year = {2018},
publisher = {Curran Associates, Inc.},
_url = {http://papers.nips.cc/paper/7334-multi-task-learning-as-multi-objective-optimization.pdf}
}


@inproceedings{DBLP:conf/icml/ChenBLR18,
  author    = {Zhao Chen and
               Vijay Badrinarayanan and
               Chen{-}Yu Lee and
               Andrew Rabinovich},
  title     = {GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep
               Multitask Networks},
  booktitle = {Proceedings of the 35th International Conference on Machine Learning,
               {ICML} 2018, Stockholmsm{\"{a}}ssan, Stockholm, Sweden, July
               10-15, 2018},
  pages     = {793--802},
  year      = {2018},
  url       = {http://proceedings.mlr.press/v80/chen18a.html},
  timestamp = {Fri, 13 Jul 2018 14:57:00 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/icml/ChenBLR18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


@inproceedings{chollet2017xception,
  title={Xception: Deep learning with depthwise separable convolutions},
  author={Chollet, Fran{\c{c}}ois},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1251--1258},
  year={2017}
}

@inproceedings{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},
  booktitle={NIPS-W},
  year={2017}
}

@article{micikevicius2017mixed,
  title={Mixed precision training},
  author={Micikevicius, Paulius and Narang, Sharan and Alben, Jonah and Diamos, Gregory and Elsen, Erich and Garcia, David and Ginsburg, Boris and Houston, Michael and Kuchaiev, Oleksii and Venkatesh, Ganesh and others},
  journal={arXiv preprint arXiv:1710.03740},
  year={2017}
}

@article{DBLP:journals/corr/abs-1801-06519,
  author    = {Arun Mallya and
               Svetlana Lazebnik},
  title     = {Piggyback: Adding Multiple Tasks to a Single, Fixed Network by Learning
               to Mask},
  journal   = {CoRR},
  volume    = {abs/1801.06519},
  year      = {2018},
  url       = {http://arxiv.org/abs/1801.06519},
  archivePrefix = {arXiv},
  eprint    = {1801.06519},
  timestamp = {Mon, 13 Aug 2018 16:48:40 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1801-06519},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/FernandoBBZHRPW17,
  title={Pathnet: Evolution channels gradient descent in super neural networks},
  author={Fernando, Chrisantha and Banarse, Dylan and Blundell, Charles and Zwols, Yori and Ha, David and Rusu, Andrei A and Pritzel, Alexander and Wierstra, Daan},
  journal={arXiv preprint arXiv:1701.08734},
  year={2017}
}

@article{10.5555/3122009.3242042,
author = {Li, Lisha and Jamieson, Kevin and DeSalvo, Giulia and Rostamizadeh, Afshin and Talwalkar, Ameet},
title = {Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization},
year = {2017},
issue_date = {January 2017},
publisher = {JMLR.org},
volume = {18},
number = {1},
issn = {1532-4435},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {6765–6816},
numpages = {52},
keywords = {deep learning, model selection, online optimization, hyperparameter optimization, infinite-armed bandits}
}


@inproceedings{10.5555/2832581.2832731,
author = {Domhan, Tobias and Springenberg, Jost Tobias and Hutter, Frank},
title = {Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves},
year = {2015},
isbn = {9781577357384},
publisher = {AAAI Press},
booktitle = {Proceedings of the 24th International Conference on Artificial Intelligence},
pages = {3460–3468},
numpages = {9},
location = {Buenos Aires, Argentina},
series = {IJCAI’15}
}


@inproceedings{ge2017optimization,
  title={On the optimization landscape of tensor decompositions},
  author={Ge, Rong and Ma, Tengyu},
  booktitle={Advances in Neural Information Processing Systems},
  pages={3653--3663},
  year={2017}
}



@inproceedings{kendall2017multi,
  title={Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics},
  author={Kendall, Alex and Gal, Yarin and Cipolla, Roberto},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition ({CVPR})},
  year={2018}
}

@InProceedings{pmlr-v119-standley20a,
  title={Which tasks should be learned together in multi-task learning?},
  author={Standley, Trevor and Zamir, Amir and Chen, Dawn and Guibas, Leonidas and Malik, Jitendra and Savarese, Silvio},
  booktitle={International Conference on Machine Learning},
  pages={9120--9132},
  year={2020},
  organization={PMLR}
}






@article{chen2021neural,
  title={Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective},
  author={Chen, Wuyang and Gong, Xinyu and Wang, Zhangyang},
  journal={arXiv preprint arXiv:2102.11535},
  year={2021}
}

@article{cai2019once,
  title={Once-for-all: Train one network and specialize it for efficient deployment},
  author={Cai, Han and Gan, Chuang and Wang, Tianzhe and Zhang, Zhekai and Han, Song},
  journal={arXiv preprint arXiv:1908.09791},
  year={2019}
}

@inproceedings{zhou2020econas,
  title={Econas: Finding proxies for economical neural architecture search},
  author={Zhou, Dongzhan and Zhou, Xinchi and Zhang, Wenwei and Loy, Chen Change and Yi, Shuai and Zhang, Xuesen and Ouyang, Wanli},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11396--11404},
  year={2020}
}

@inproceedings{yang2020cars,
  title={Cars: Continuous evolution for efficient neural architecture search},
  author={Yang, Zhaohui and Wang, Yunhe and Chen, Xinghao and Shi, Boxin and Xu, Chao and Xu, Chunjing and Tian, Qi and Xu, Chang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={1829--1838},
  year={2020}
}

@inproceedings{zhang2020overcoming,
  title={Overcoming multi-model forgetting in one-shot nas with diversity maximization},
  author={Zhang, Miao and Li, Huiqi and Pan, Shirui and Chang, Xiaojun and Su, Steven},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7809--7818},
  year={2020}
}

@article{nguyen2020wide,
  title={Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth},
  author={Nguyen, Thao and Raghu, Maithra and Kornblith, Simon},
  journal={arXiv preprint arXiv:2010.15327},
  year={2020}
}

@inproceedings{wan2020fbnetv2,
  title={Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions},
  author={Wan, Alvin and Dai, Xiaoliang and Zhang, Peizhao and He, Zijian and Tian, Yuandong and Xie, Saining and Wu, Bichen and Yu, Matthew and Xu, Tao and Chen, Kan and others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12965--12974},
  year={2020}
}

@article{DBLP:journals/corr/abs-2101-07415,
  title={ES-ENAS: Combining Evolution Strategies with Neural Architecture Search at No Extra Cost for Reinforcement Learning},
  author={Song, Xingyou and Choromanski, Krzysztof and Parker-Holder, Jack and Tang, Yunhao and Peng, Daiyi and Jain, Deepali and Gao, Wenbo and Pacchiano, Aldo and Sarlos, Tamas and Yang, Yuxiang},
  journal={arXiv preprint arXiv:2101.07415},
  year={2021}
}}

@article{zhao2020few,
  title={Few-shot neural architecture search},
  author={Zhao, Yiyang and Wang, Linnan and Tian, Yuandong and Fonseca, Rodrigo and Guo, Tian},
  journal={arXiv preprint arXiv:2006.06863},
  year={2020}
}

@article{Nguyen2020_cbc,
  title={Optimal transport kernels for sequential and parallel neural architecture search},
  author={Nguyen, Vu and Le, Tam and Yamada, Makoto and Osborne, Michael A},
  journal={arXiv preprint arXiv:2006.07593},
  year={2020}
}

@ARTICLE{9336721,
  author={Y. {Sun} and X. {Sun} and Y. {Fang} and G. G. {Yen} and Y. {Liu}},
  journal={IEEE Transactions on Evolutionary Computation}, 
  title={A Novel Training Protocol for Performance Predictors of Evolutionary Neural Architecture Search Algorithms}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TEVC.2021.3055076}}
  
@article{awad2020differential,
  title={Differential Evolution for Neural Architecture Search},
  author={Awad, Noor and Mallik, Neeratyoy and Hutter, Frank},
  journal={arXiv preprint arXiv:2012.06400},
  year={2020}
}

@article{SulaimanSadiq2020_ibb,
  title={DEff-ARTS: Differentiable Efficient ARchiTecture Search},
  author={Sadiq, Sulaiman and Maji, Partha and Hare, Jonathan and Merrett, Geoff},
  year={2020}
}

@inproceedings{bender2020can,
  title={Can weight sharing outperform random architecture search? an investigation with tunas},
  author={Bender, Gabriel and Liu, Hanxiao and Chen, Bo and Chu, Grace and Cheng, Shuyang and Kindermans, Pieter-Jan and Le, Quoc V},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14323--14332},
  year={2020}
}

@article{xu2019pc,
  title={PC-DARTS: Partial channel connections for memory-efficient architecture search},
  author={Xu, Yuhui and Xie, Lingxi and Zhang, Xiaopeng and Chen, Xin and Qi, Guo-Jun and Tian, Qi and Xiong, Hongkai},
  journal={arXiv preprint arXiv:1907.05737},
  year={2019}
}

@inproceedings{le2021task,
  title={Task-aware neural architecture search},
  author={Le, Cat P and Soltani, Mohammadreza and Ravier, Robert and Tarokh, Vahid},
  booktitle={ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={4090--4094},
  year={2021},
  organization={IEEE}
}

@inproceedings{he2020milenas,
  title={Milenas: Efficient neural architecture search via mixed-level reformulation},
  author={He, Chaoyang and Ye, Haishan and Shen, Li and Zhang, Tong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11993--12002},
  year={2020}
}

@inproceedings{chen2018coupled,
  title={Coupled end-to-end transfer learning with generalized fisher information},
  author={Chen, Shixing and Zhang, Caojin and Dong, Ming},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={4329--4338},
  year={2018}
}

@article{kirkpatrick2017overcoming,
  title={Overcoming catastrophic forgetting in neural networks},
  author={Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and others},
  journal={Proceedings of the national academy of sciences},
  volume={114},
  number={13},
  pages={3521--3526},
  year={2017},
  publisher={National Acad Sciences}
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}

@inproceedings{li2020random,
  title={Random search and reproducibility for neural architecture search},
  author={Li, Liam and Talwalkar, Ameet},
  booktitle={Uncertainty in artificial intelligence},
  pages={367--377},
  year={2020},
  organization={PMLR}
}

@inproceedings{finn2017model,
  title={Model-agnostic meta-learning for fast adaptation of deep networks},
  author={Finn, Chelsea and Abbeel, Pieter and Levine, Sergey},
  booktitle={International Conference on Machine Learning},
  pages={1126--1135},
  year={2017},
  organization={PMLR}
}

@inproceedings{rodriguez2020embedding,
  title={Embedding propagation: Smoother manifold for few-shot classification},
  author={Rodr{\'\i}guez, Pau and Laradji, Issam and Drouin, Alexandre and Lacoste, Alexandre},
  booktitle={European Conference on Computer Vision},
  pages={121--138},
  year={2020},
  organization={Springer}
}

@article{chen2020new,
  title={A new meta-baseline for few-shot learning},
  author={Chen, Yinbo and Wang, Xiaolong and Liu, Zhuang and Xu, Huijuan and Darrell, Trevor},
  journal={arXiv preprint arXiv:2003.04390},
  year={2020}
}

@article{vinyals2016matching,
  title={Matching networks for one shot learning},
  author={Vinyals, Oriol and Blundell, Charles and Lillicrap, Timothy and Wierstra, Daan and others},
  journal={Advances in neural information processing systems},
  volume={29},
  pages={3630--3638},
  year={2016}
}

@article{snell2017prototypical,
  title={Prototypical networks for few-shot learning},
  author={Snell, Jake and Swersky, Kevin and Zemel, Richard S},
  journal={arXiv preprint arXiv:1703.05175},
  year={2017}
}

@inproceedings{chen2021metabaseline,
  title={Meta-baseline: exploring simple meta-learning for few-shot learning},
  author={Chen, Yinbo and Liu, Zhuang and Xu, Huijuan and Darrell, Trevor and Wang, Xiaolong},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2021}
}

@inproceedings{tian2020rethinking,
  title={Rethinking few-shot image classification: a good embedding is all you need?},
  author={Tian, Yonglong and Wang, Yue and Krishnan, Dilip and Tenenbaum, Joshua B and Isola, Phillip},
  booktitle={Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XIV 16},
  pages={266--282},
  year={2020},
  organization={Springer}
}

@article{2021arXiv210312827L,
  title={Fisher Task Distance and its Application in Neural Architecture Search},
  author={Le, Cat P and Soltani, Mohammadreza and Dong, Juncheng and Tarokh, Vahid},
  journal={IEEE Access},
  volume={10},
  pages={47235--47249},
  year={2022},
  publisher={IEEE}
}

@article{ren2018meta,
  title={Meta-learning for semi-supervised few-shot classification},
  author={Ren, Mengye and Triantafillou, Eleni and Ravi, Sachin and Snell, Jake and Swersky, Kevin and Tenenbaum, Joshua B and Larochelle, Hugo and Zemel, Richard S},
  journal={arXiv preprint arXiv:1803.00676},
  year={2018}
}

@article{bertinetto2018meta,
  title={Meta-learning with differentiable closed-form solvers},
  author={Bertinetto, Luca and Henriques, Joao F and Torr, Philip HS and Vedaldi, Andrea},
  journal={arXiv preprint arXiv:1805.08136},
  year={2018}
}

@inproceedings{qiao2018few,
  title={Few-shot image recognition by predicting parameters from activations},
  author={Qiao, Siyuan and Liu, Chenxi and Shen, Wei and Yuille, Alan L},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={7229--7238},
  year={2018}
}

@article{rusu2018meta,
  title={Meta-learning with latent embedding optimization},
  author={Rusu, Andrei A and Rao, Dushyant and Sygnowski, Jakub and Vinyals, Oriol and Pascanu, Razvan and Osindero, Simon and Hadsell, Raia},
  journal={arXiv preprint arXiv:1807.05960},
  year={2018}
}

@article{chen2019closer,
  title={A closer look at few-shot classification},
  author={Chen, Wei-Yu and Liu, Yen-Cheng and Kira, Zsolt and Wang, Yu-Chiang Frank and Huang, Jia-Bin},
  journal={arXiv preprint arXiv:1904.04232},
  year={2019}
}

@article{mishra2017simple,
  title={A simple neural attentive meta-learner},
  author={Mishra, Nikhil and Rohaninejad, Mostafa and Chen, Xi and Abbeel, Pieter},
  journal={arXiv preprint arXiv:1707.03141},
  year={2017}
}

@inproceedings{munkhdalai2018rapid,
  title={Rapid adaptation with conditionally shifted neurons},
  author={Munkhdalai, Tsendsuren and Yuan, Xingdi and Mehri, Soroush and Trischler, Adam},
  booktitle={International Conference on Machine Learning},
  pages={3664--3673},
  year={2018},
  organization={PMLR}
}

@article{oreshkin2018tadam,
  title={Tadam: Task dependent adaptive metric for improved few-shot learning},
  author={Oreshkin, Boris N and Rodriguez, Pau and Lacoste, Alexandre},
  journal={arXiv preprint arXiv:1805.10123},
  year={2018}
}

@inproceedings{sun2019meta,
  title={Meta-transfer learning for few-shot learning},
  author={Sun, Qianru and Liu, Yaoyao and Chua, Tat-Seng and Schiele, Bernt},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={403--412},
  year={2019}
}

@inproceedings{lee2019meta,
  title={Meta-learning with differentiable convex optimization},
  author={Lee, Kwonjoon and Maji, Subhransu and Ravichandran, Avinash and Soatto, Stefano},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={10657--10665},
  year={2019}
}

@inproceedings{lee2020self,
  title={Self-supervised label augmentation via input transformations},
  author={Lee, Hankook and Hwang, Sung Ju and Shin, Jinwoo},
  booktitle={International Conference on Machine Learning},
  pages={5714--5724},
  year={2020},
  organization={PMLR}
}

@inproceedings{xu2020attentional,
  title={Attentional Constellation Nets for Few-Shot Learning},
  author={Xu, Weijian and Wang, Huaijin and Tu, Zhuowen and others},
  booktitle={International Conference on Learning Representations},
  year={2020}
}

@inproceedings{ravichandran2019few,
  title={Few-shot learning with embedded class models and shot-free meta training},
  author={Ravichandran, Avinash and Bhotika, Rahul and Soatto, Stefano},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={331--339},
  year={2019}
}

@inproceedings{sung2018learning,
  title={Learning to compare: Relation network for few-shot learning},
  author={Sung, Flood and Yang, Yongxin and Zhang, Li and Xiang, Tao and Torr, Philip HS and Hospedales, Timothy M},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={1199--1208},
  year={2018}
}

@article{dhillon2019baseline,
  title={A baseline for few-shot image classification},
  author={Dhillon, Guneet S and Chaudhari, Pratik and Ravichandran, Avinash and Soatto, Stefano},
  journal={arXiv preprint arXiv:1909.02729},
  year={2019}
}

@inproceedings{qiao2019transductive,
  title={Transductive episodic-wise adaptive metric for few-shot learning},
  author={Qiao, Limeng and Shi, Yemin and Li, Jia and Wang, Yaowei and Huang, Tiejun and Tian, Yonghong},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={3603--3612},
  year={2019}
}

@inproceedings{Ravi2017OptimizationAA,
  title={Optimization as a model for few-shot learning},
  author={Ravi, Sachin and Larochelle, Hugo},
  booktitle={ICLR},
  year={2017}
}

@inproceedings{santoro2016meta,
  title={Meta-learning with memory-augmented neural networks},
  author={Santoro, Adam and Bartunov, Sergey and Botvinick, Matthew and Wierstra, Daan and Lillicrap, Timothy},
  booktitle={International conference on machine learning},
  pages={1842--1850},
  year={2016},
  organization={PMLR}
}

@article{grant2018recasting,
  title={Recasting gradient-based meta-learning as hierarchical bayes},
  author={Grant, Erin and Finn, Chelsea and Levine, Sergey and Darrell, Trevor and Griffiths, Thomas},
  journal={arXiv preprint arXiv:1801.08930},
  year={2018}
}

@inproceedings{rizve2021exploring,
  title={Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning},
  author={Rizve, Mamshad Nayeem and Khan, Salman and Khan, Fahad Shahbaz and Shah, Mubarak},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={10836--10846},
  year={2021}
}

@inproceedings{Siamese2015,
  title={Siamese neural networks for one-shot image recognition},
  author={Koch, Gregory and Zemel, Richard and Salakhutdinov, Ruslan and others},
  booktitle={ICML deep learning workshop},
  volume={2},
  year={2015},
  organization={Lille}
}

@article{nichol2018first,
  title={On first-order meta-learning algorithms},
  author={Nichol, Alex and Achiam, Joshua and Schulman, John},
  journal={arXiv preprint arXiv:1803.02999},
  year={2018}
}

@inproceedings{mangla2020charting,
  title={Charting the right manifold: Manifold mixup for few-shot learning},
  author={Mangla, Puneet and Kumari, Nupur and Sinha, Abhishek and Singh, Mayank and Krishnamurthy, Balaji and Balasubramanian, Vineeth N},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={2218--2227},
  year={2020}
}

@inproceedings{verma2019manifold,
  title={Manifold mixup: Better representations by interpolating hidden states},
  author={Verma, Vikas and Lamb, Alex and Beckham, Christopher and Najafi, Amir and Mitliagkas, Ioannis and Lopez-Paz, David and Bengio, Yoshua},
  booktitle={International Conference on Machine Learning},
  pages={6438--6447},
  year={2019},
  organization={PMLR}
}

@article{russakovsky2015imagenet,
  title={Imagenet large scale visual recognition challenge},
  author={Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and others},
  journal={International journal of computer vision},
  volume={115},
  number={3},
  pages={211--252},
  year={2015},
  publisher={Springer}
}

@inproceedings{bateni2020improved,
  title={Improved few-shot visual classification},
  author={Bateni, Peyman and Goyal, Raghav and Masrani, Vaden and Wood, Frank and Sigal, Leonid},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14493--14502},
  year={2020}
}

@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{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},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}\textbf{}
  
@ARTICLE{9766163,  author={Le, Cat P. and Soltani, Mohammadreza and Dong, Juncheng and Tarokh, Vahid},  journal={IEEE Access},   title={Fisher Task Distance and its Application in Neural Architecture Search},   year={2022},  volume={10},  number={},  pages={47235-47249},  doi={10.1109/ACCESS.2022.3171741}}

@ARTICLE{2021arXiv210300241L,
       author = {{Le}, Cat P. and {Soltani}, Mohammadreza and {Ravier}, Robert and {Tarokh}, Vahid},
        title = "{Improved Automated Machine Learning from Transfer Learning}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition},
         year = 2021,
        month = feb,
          eid = {arXiv:2103.00241},
        pages = {arXiv:2103.00241},
archivePrefix = {arXiv},
       eprint = {2103.00241},
 primaryClass = {cs.LG},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210300241L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@inproceedings{le2022task,
  title={Task Affinity with Maximum Bipartite Matching in Few-Shot Learning},
  author={Le, Cat Phuoc and Dong, Juncheng and Soltani, Mohammadreza and Tarokh, Vahid},
  booktitle={International Conference on Learning Representations},
  year={2022}
}

@book{pearl,
author = {Pearl, Judea},
title = {Causality: Models, Reasoning and Inference},
year = {2009},
isbn = {052189560X},
publisher = {Cambridge University Press},
address = {USA},
edition = {2nd},
}

@article{rubin,
  title={Bayesian inference for causal effects: The role of randomization},
  author={Rubin, Donald B},
  journal={The Annals of statistics},
  pages={34--58},
  year={1978},
  publisher={JSTOR}
}

@article{imbens,
  title={Nonparametric estimation of average treatment effects under exogeneity: A review},
  author={Imbens, Guido W},
  journal={Review of Economics and statistics},
  volume={86},
  number={1},
  pages={4--29},
  year={2004},
  publisher={MIT Press 238 Main St., Suite 500, Cambridge, MA 02142-1046, USA journals~…}
}

@inproceedings{shalit,
  title={Estimating individual treatment effect: generalization bounds and algorithms},
  author={Shalit, Uri and Johansson, Fredrik D and Sontag, David},
  booktitle={International Conference on Machine Learning},
  pages={3076--3085},
  year={2017},
  organization={PMLR}
}

@article{propensity,
  title={The central role of the propensity score in observational studies for causal effects},
  author={Rosenbaum, Paul R and Rubin, Donald B},
  journal={Biometrika},
  volume={70},
  number={1},
  pages={41--55},
  year={1983},
  publisher={Oxford University Press}
}

@inproceedings{johanson,
  title={Learning representations for counterfactual inference},
  author={Johansson, Fredrik and Shalit, Uri and Sontag, David},
  booktitle={International conference on machine learning},
  pages={3020--3029},
  year={2016},
  organization={PMLR}
}

@misc{representation,
  doi = {10.48550/ARXIV.2102.11107},
  
  url = {https://arxiv.org/abs/2102.11107},
  
  author = {Schölkopf, Bernhard and Locatello, Francesco and Bauer, Stefan and Ke, Nan Rosemary and Kalchbrenner, Nal and Goyal, Anirudh and Bengio, Yoshua},
  
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Towards Causal Representation Learning},
  
  publisher = {arXiv},
  
  year = {2021},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{athey,
  title={Estimation and inference of heterogeneous treatment effects using random forests},
  author={Wager, Stefan and Athey, Susan},
  journal={Journal of the American Statistical Association},
  volume={113},
  number={523},
  pages={1228--1242},
  year={2018},
  publisher={Taylor \& Francis}
}

@article{doubly,
  title={Doubly robust estimation of causal effects},
  author={Funk, Michele Jonsson and Westreich, Daniel and Wiesen, Chris and St{\"u}rmer, Til and Brookhart, M Alan and Davidian, Marie},
  journal={American journal of epidemiology},
  volume={173},
  number={7},
  pages={761--767},
  year={2011},
  publisher={Oxford University Press}
}

@inproceedings{le2020supervised,
  title={Supervised Encoding for Discrete Representation Learning},
  author={Le, Cat P and Zhou, Yi and Ding, Jie and Tarokh, Vahid},
  booktitle={ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={3447--3451},
  year={2020},
  organization={IEEE}
}

@article{le2022fisher,
  title={Fisher Task Distance and its Application in Neural Architecture Search},
  author={Le, Cat P and Soltani, Mohammadreza and Dong, Juncheng and Tarokh, Vahid},
  journal={IEEE Access},
  volume={10},
  pages={47235--47249},
  year={2022},
  publisher={IEEE}
}

@article{rubin1978bayesian,
  title={Bayesian inference for causal effects: The role of randomization},
  author={Rubin, Donald B},
  journal={The Annals of statistics},
  pages={34--58},
  year={1978},
  publisher={JSTOR}
}

@incollection{lewis,
	Address = {Cambridge},
	Author = {David Lewis},
	Booktitle = {Causation and Counterfactuals},
	Editor = {John Collins and Ned Hall and L. A. Paul},
	Pages = {75-106},
	Publisher = {{MIT} Press},
	Title = {Causation as Influence},
	Year = {2004}}

@article{alaa,
  title={Bayesian inference of individualized treatment effects using multi-task gaussian processes},
  author={Alaa, Ahmed M and Van Der Schaar, Mihaela},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@inproceedings{ganite,
  title={GANITE: Estimation of individualized treatment effects using generative adversarial nets},
  author={Yoon, Jinsung and Jordon, James and Van Der Schaar, Mihaela},
  booktitle={International Conference on Learning Representations},
  year={2018}
}

@article{hill,
  title={Bayesian nonparametric modeling for causal inference},
  author={Hill, Jennifer L},
  journal={Journal of Computational and Graphical Statistics},
  volume={20},
  number={1},
  pages={217--240},
  year={2011},
  publisher={Taylor \& Francis}
}

@article{cevae,
  title={Causal effect inference with deep latent-variable models},
  author={Louizos, Christos and Shalit, Uri and Mooij, Joris M and Sontag, David and Zemel, Richard and Welling, Max},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{robins1987,
  title={A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect},
  author={Robins, James},
  journal={Mathematical modelling},
  volume={7},
  number={9-12},
  pages={1393--1512},
  year={1986},
  publisher={Elsevier}
}

@book{villani,
  title={Optimal transport: old and new},
  author={Villani, C{\'e}dric},
  volume={338},
  year={2009},
  publisher={Springer}
}

@article{Gockenbach2009NewtonsLO,
  title={Newton’s law of heating and the heat equation},
  author={Mark S. Gockenbach and Kristin Schmidtke},
  journal={Involve, A Journal of Mathematics},
  year={2009},
  volume={2},
  pages={419-437}
  }

@book{imbensbook,
  title={Causal inference in statistics, social, and biomedical sciences},
  author={Imbens, Guido W and Rubin, Donald B},
  year={2015},
  publisher={Cambridge University Press}
}

@inproceedings{Bareinboim2021,
  title={Transportability of causal effects: Completeness results},
  author={Bareinboim, Elias and Pearl, Judea},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={26},
  pages={698--704},
  year={2012}
}

@article{holland1986,
  title={Statistics and causal inference},
  author={Holland, Paul W},
  journal={Journal of the American statistical Association},
  volume={81},
  number={396},
  pages={945--960},
  year={1986},
  publisher={Taylor \& Francis}
}

@article{rubin1974,
  title={Estimating causal effects of treatments in randomized and nonrandomized studies.},
  author={Rubin, Donald B},
  journal={Journal of educational Psychology},
  volume={66},
  number={5},
  pages={688},
  year={1974},
  publisher={American Psychological Association}
}

@article{Ben-David2010,
  title={A theory of learning from different domains},
  author={Ben-David, Shai and Blitzer, John and Crammer, Koby and Kulesza, Alex and Pereira, Fernando and Vaughan, Jennifer Wortman},
  journal={Machine learning},
  volume={79},
  number={1},
  pages={151--175},
  year={2010},
  publisher={Springer}
}


@article{neyman1923,
  title={Sur les applications de la th{\'e}orie des probabilit{\'e}s aux experiences agricoles: Essai des principes},
  author={Neyman, Jersey},
  journal={Roczniki Nauk Rolniczych},
  volume={10},
  number={1},
  pages={1--51},
  year={1923}
}

@article{donald2005causal,
  title={Causal inference using potential outcomes},
  author={Donald, Rubin},
  journal={Journal of the American Statistical Association},
  volume={100},
  number={469},
  pages={322--31},
  year={2005}
}

@article{athey2016recursive,
  title={Recursive partitioning for heterogeneous causal effects},
  author={Athey, Susan and Imbens, Guido},
  journal={Proceedings of the National Academy of Sciences},
  volume={113},
  number={27},
  pages={7353--7360},
  year={2016},
  publisher={National Acad Sciences}
}

@article{bica2022transfer,
  title={Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation},
  author={Bica, Ioana and van der Schaar, Mihaela},
  journal={arXiv preprint arXiv:2210.06183},
  year={2022}
}

@article{aglietti2020multi,
  title={Multi-task causal learning with Gaussian processes},
  author={Aglietti, Virginia and Damoulas, Theodoros and {\'A}lvarez, Mauricio and Gonz{\'a}lez, Javier},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={6293--6304},
  year={2020}
}

@inproceedings{vo2022adaptive,
  title={Adaptive Multi-Source Causal Inference from Observational Data},
  author={Vo, Thanh Vinh and Wei, Pengfei and Hoang, Trong Nghia and Leong, Tze Yun},
  booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
  pages={1975--1985},
  year={2022}
}

