@article{girshick2015fast,
  title={Fast r-cnn},
  author={Girshick, R},
  journal={arXiv preprint arXiv:1504.08083},
  year={2015}
}
@article{Heckman01:Heterogeneity,
  author = {James J. Heckman},
  journal = {Journal of Political Economy},
  number = {4},
  pages = {673--748},
  publisher = {The University of Chicago Press},
  title = {Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture},
  volume = {109},
  year = {2001}
}
@inproceedings{ha2016hypernetworks,
  title={HyperNetworks},
  author={Ha, David and Dai, Andrew M and Le, Quoc V},
  booktitle={International Conference on Learning Representations},
  year={2016}
}
@inproceedings{Muandet13:DG, 
    title = {Domain Generalization via Invariant Feature Representation}, 
    author = {Krikamol Muandet and David Balduzzi and Bernhard Schölkopf}, 
    booktitle = {Proceedings of the 30th International Conference on Machine Learning}, 
    pages = {10--18}, 
    year = {2013}, 
    volume = {28}, 
    number = {1}, 
    series = {Proceedings of Machine Learning Research}, 
}

@book{Vapnik95:SLT,
  author = {Vapnik, Vladimir N.},
  title = {The Nature of Statistical Learning Theory},
  year = {1995},
  publisher = {Springer-Verlag},
  address = {Berlin, Heidelberg}
}

@inproceedings{Vapnik91:ERM,
  author = {Vapnik, V.},
  booktitle = {Advances in Neural Information Processing Systems},
  publisher = {Morgan-Kaufmann},
  title = {Principles of Risk Minimization for Learning Theory},
  volume = {4},
  year = {1991}
}

@book{Candela09:DS,
  author = {Quionero-Candela, Joaquin and Sugiyama, Masashi and Schwaighofer, Anton and Lawrence, Neil D.},
  title = {Dataset Shift in Machine Learning},
  year = {2009},
  publisher = {The MIT Press},
}

@inproceedings{Koh21:WILDS,
  title = 	 {{WILDS}: A Benchmark of in-the-Wild Distribution Shifts},
  author =       {Koh, Pang Wei and Sagawa, Shiori and Marklund, Henrik and Xie, Sang Michael and Zhang, Marvin and Balsubramani, Akshay and Hu, Weihua and Yasunaga, Michihiro and Phillips, Richard Lanas and Gao, Irena and Lee, Tony and David, Etienne and Stavness, Ian and Guo, Wei and Earnshaw, Berton and Haque, Imran and Beery, Sara M and Leskovec, Jure and Kundaje, Anshul and Pierson, Emma and Levine, Sergey and Finn, Chelsea and Liang, Percy},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {5637--5664},
  year = 	 {2021},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR}
}

@inproceedings{Beery18:WildLife,
  author = {Beery, Sara and Van Horn, Grant and Perona, Pietro},
  title = {Recognition in Terra Incognita},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  month = {September},
  year = {2018}
}

@article{Beery20:iWildCam,
  author    = {Sara Beery and
               Elijah Cole and
               Arvi Gjoka},
  title     = {The {iWildCam} 2020 Competition Dataset},
  journal   = {CoRR},
  year      = {2020},
  url       = {https://arxiv.org/abs/2004.10340},
  eprinttype = {arXiv},
  eprint    = {2004.10340},
  timestamp = {Tue, 28 Apr 2020 16:10:02 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2004-10340.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{Szegedy13:Properties,
  title={Intriguing properties of neural networks},
  author={Szegedy, Christian and Zaremba, Wojciech and Sutskever, Ilya and Bruna, Joan and Erhan, Dumitru and Goodfellow, Ian and Fergus, Rob},
  journal={arXiv preprint arXiv:1312.6199},
  year={2013}
}
@article{Goodfellow14:Explaining,
  title={Explaining and harnessing adversarial examples},
  author={Goodfellow, Ian J and Shlens, Jonathon and Szegedy, Christian},
  journal={arXiv preprint arXiv:1412.6572},
  year={2014}
}

@article{Gagnon-Audet22:WOODSBF,
  title={WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks},
  author={Jean-Christophe Gagnon-Audet and Kartik Ahuja and Mohammad Javad Darvishi Bayazi and G. Dumas and Irina Rish},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.09978}
}

@inproceedings{Eastwood22:QRM,
   author = {Eastwood, Cian and Robey, Alexander and Singh, Shashank and von K\"{u}gelgen, Julius and Hassani, Hamed and Pappas, George J. and Sch\"{o}lkopf, Bernhard},
   booktitle = {Advances in Neural Information Processing Systems},
   pages = {17340--17358},
   publisher = {Curran Associates, Inc.},
   title = {Probable Domain Generalization via Quantile Risk Minimization},
   volume = {35},
   year = {2022}
}

@incollection{Blanchard11:Generalize,
    title   = {Generalizing from Several Related Classification Tasks to a New Unlabeled Sample},
    author                   = {G. Blanchard and G. Lee and C. Scott},
    booktitle                = {Advances in Neural Information Processing Systems (NIPS)},
    year                     = {2011},
    pages                    = {2178--2186}
}

@article{Blanchard21:DG-MTL,
  author  = {Gilles Blanchard and Aniket Anand Deshmukh and Urun Dogan and Gyemin Lee and Clayton Scott},
  title   = {Domain Generalization by Marginal Transfer Learning},
  journal = {Journal of Machine Learning Research},
  year    = {2021},
  volume  = {22},
  number  = {2},
  pages   = {1--55}
}

@inproceedings{Zhang21:ARM,
  author = {Zhang, Marvin and Marklund, Henrik and Dhawan, Nikita and Gupta, Abhishek and Levine, Sergey and Finn, Chelsea},
  booktitle = {Advances in Neural Information Processing Systems},
  pages = {23664--23678},
  publisher = {Curran Associates, Inc.},
  title = {Adaptive Risk Minimization: Learning to Adapt to Domain Shift},
  volume = {34},
  year = {2021}
}

@InProceedings{Ahuja20:IRM-Games,
  title = 	 {Invariant Risk Minimization Games},
  author =       {Ahuja, Kartik and Shanmugam, Karthikeyan and Varshney, Kush and Dhurandhar, Amit},
  booktitle = 	 {Proceedings of the 37th International Conference on Machine Learning},
  pages = 	 {145--155},
  year = 	 {2020},
  volume = 	 {119},
  series = 	 {Proceedings of Machine Learning Research},
  publisher =    {PMLR}
}


@article{Arjovsky19:IRM,
  title={Invariant Risk Minimization},
  author={Mart{\'i}n Arjovsky and L{\'e}on Bottou and Ishaan Gulrajani and David Lopez-Paz},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.02893}
}

@inproceedings{Gulrajani21:DG,
  title={In Search of Lost Domain Generalization},
  author={Ishaan Gulrajani and David Lopez-Paz},
  booktitle={International Conference on Learning Representations},
  year={2021},
  url={https://openreview.net/forum?id=lQdXeXDoWtI}
}

@book{Ben-Tal09:RO,
  author = {Ben-Tal, Aharon and Ghaoui, Laurent El and Nemirovski, Arkadi},
  pages = {1--542},
  publisher = {Princeton University Press},
  title = {Robust Optimization},
  volume = 28,
  year = 2009
}

@inproceedings{Sagawa20:DRO,
  title={Distributionally Robust Neural Networks},
  author={Shiori Sagawa and Pang Wei Koh and Tatsunori B. Hashimoto and Percy Liang},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=ryxGuJrFvS}
}

@inproceedings{Lin19:Pareto-MTL,
 author = {Lin, Xi and Zhen, Hui-Ling and Li, Zhenhua and Zhang, Qing-Fu and Kwong, Sam},
 booktitle = {Advances in Neural Information Processing Systems},
 publisher = {Curran Associates, Inc.},
 title = {Pareto Multi-Task Learning},
 volume = {32},
 year = {2019}
}

@inproceedings{Ma20:Efficient-Pareto-MTL,
  title={Efficient Continuous Pareto Exploration in Multi-Task Learning},
  author={Ma, Pingchuan and Du, Tao and Matusik, Wojciech},
  booktitle={International Conference on Machine Learning},
  pages={6522--6531},
  year={2020},
  organization={PMLR}
}

@inproceedings{Sener18:MTL-MOO,
  author = {Sener, Ozan and Koltun, Vladlen},
  title = {Multi-Task Learning as Multi-Objective Optimization},
  year = {2018},
  publisher = {Curran Associates Inc.},
  address = {Red Hook, NY, USA},
  booktitle = {Proceedings of the 32nd International Conference on Neural Information Processing Systems},
  pages = {525--536},
  numpages = {12}
}

@book{Walley91:SRIP,
	author = {Peter Walley},
	publisher = {Chapman \& Hall},
	title = {Statistical Reasoning with Imprecise Probabilities},
	year = {1991}
}

@article{Pareto1897:Pareto,
 title = {The New Theories of Economics},
 author = {Pareto, Vilfredo},
 year = {1897},
 journal = {Journal of Political Economy},
 volume = {5}
}

@inproceedings{Robey22:PRL,
  title={Probabilistically Robust Learning: Balancing Average and Worst-case Performance},
  author={Robey, Alexander and Chamon, Luiz and Pappas, George J and Hassani, Hamed},
  booktitle={International Conference on Machine Learning},
  pages={18667--18686},
  year={2022},
  organization={PMLR}
}

@article{Li23:TERM,
  author  = {Tian Li and Ahmad Beirami and Maziar Sanjabi and Virginia Smith},
  title   = {On Tilted Losses in Machine Learning: Theory and Applications},
  journal = {Journal of Machine Learning Research},
  year    = {2023},
  volume  = {24},
  number  = {142},
  pages   = {1--79}
}

@inproceedings{Dosovitskiy20:YOTO,
  title={You Only Train Once: Loss-Conditional Training of Deep Networks},
  author={Alexey Dosovitskiy and Josip Djolonga},
  booktitle={International Conference on Learning Representations},
  year={2020}
}

@article{Rojas-Carulla18:CausalTransfer,
  author = {Rojas-Carulla, Mateo and Sch\"{o}lkopf, Bernhard and Turner, Richard and Peters, Jonas},
  title = {Invariant Models for Causal Transfer Learning},
  year = {2018},
  publisher = {JMLR.org},
  volume = {19},
  number = {1},
  journal = {Journal of Machine Learning Research},
  pages = {1309--1342},
  numpages = {34}
}

@phdthesis{Arjovsky19:OOD,
  author       = {Martin Arjovsky}, 
  title        = {Out of Distribution Generalization in Machine Learning},
  school       = {Courant Institute of Mathematical Sciences, New York University},
  year         = 2019
}

@article{Peters16:ICP,
  author = {Peters, Jonas and BÃŒhlmann, Peter and Meinshausen, Nicolai},
  title = {Causal inference by using invariant prediction: identification and confidence intervals},
  journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume = {78},
  number = {5},
  pages = {947--1012},
  year = {2016}
}


@InProceedings{Kamath21:IRM-I,
  title = 	 {Does Invariant Risk Minimization Capture Invariance?},
  author =       {Kamath, Pritish and Tangella, Akilesh and Sutherland, Danica and Srebro, Nathan},
  booktitle = 	 {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {4069--4077},
  year = 	 {2021},
  volume = 	 {130},
  publisher =    {PMLR}
}


@InProceedings{Zhang20:HyperMOO,
  title = {Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization},
  author = {Zhang, Richard and Golovin, Daniel},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  pages = {11096--11105},
  year = {2020},
  volume = {119},
  publisher = {PMLR}
}

@inproceedings{Chen23:Pareto-IRM,
  title={Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization},
  author={Yongqiang Chen and Kaiwen Zhou and Yatao Bian and Binghui Xie and Bingzhe Wu and Yonggang Zhang and MA KAILI and Han Yang and Peilin Zhao and Bo Han and James Cheng},
  booktitle={The Eleventh International Conference on Learning Representations },
  year={2023}
}

@misc{Eastwood23:Spuriosity,
      title={Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features}, 
      author={Cian Eastwood and Shashank Singh and Andrei Liviu Nicolicioiu and Marin Vlastelica and Julius von K\"ugelgen and Bernhard Sch\"olkopf},
      year={2023},
      eprint={2307.09933},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@inproceedings{Rosenfeld21:IRM-Risk,
  title={The Risks of Invariant Risk Minimization},
  author={Elan Rosenfeld and Pradeep Kumar Ravikumar and Andrej Risteski},
  booktitle={International Conference on Learning Representations},
  year={2021}
}


@InProceedings{Liu21:HRM,
  title = {Heterogeneous Risk Minimization},
  author = {Liu, Jiashuo and Hu, Zheyuan and Cui, Peng and Li, Bo and Shen, Zheyan},
  booktitle = {Proceedings of the 38th International Conference on Machine Learning},
  pages = {6804--6814},
  year = {2021},
  volume = {139},
  publisher = {PMLR}
}


@InProceedings{Mahajan21:DG-CM,
  title = {Domain Generalization using Causal Matching},
  author = {Mahajan, Divyat and Tople, Shruti and Sharma, Amit},
  booktitle = {Proceedings of the 38th International Conference on Machine Learning},
  pages = {7313--7324},
  year = {2021},
  volume = {139},
  publisher = {PMLR}  
}


@article{Heinze-Deml21:CVP,
author = {Heinze-Deml, Christina and Meinshausen, Nicolai},
title = {Conditional variance penalties and domain shift robustness},
year = {2021},
publisher = {Kluwer Academic Publishers},
volume = {110},
number = {2},
journal = {Machine Learning},
pages = {303--348},
numpages = {46}
}


@InProceedings{Sun20:Test-Time,
  title = {Test-Time Training with Self-Supervision for Generalization under Distribution Shifts},
  author = {Sun, Yu and Wang, Xiaolong and Liu, Zhuang and Miller, John and Efros, Alexei and Hardt, Moritz},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  pages = {9229--9248},
  year = {2020},
  volume = 	 {119},
  publisher =    {PMLR}
}


@inproceedings{Liu21:TTT,
  author = {Liu, Yuejiang and Kothari, Parth and van Delft, Bastien and Bellot-Gurlet, Baptiste and Mordan, Taylor and Alahi, Alexandre},
  booktitle = {Advances in Neural Information Processing Systems},
  pages = {21808--21820},
  publisher = {Curran Associates, Inc.},
  title = {{TTT}++: When Does Self-Supervised Test-Time Training Fail or Thrive?},
  volume = {34},
  year = {2021}
}


@inproceedings{Wang21:Tent,
  title={Tent: Fully Test-Time Adaptation by Entropy Minimization},
  author={Dequan Wang and Evan Shelhamer and Shaoteng Liu and Bruno Olshausen and Trevor Darrell},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

@InProceedings{Chen23:Improved-TTT,
  author    = {Chen, Liang and Zhang, Yong and Song, Yibing and Shan, Ying and Liu, Lingqiao},
  title     = {Improved Test-Time Adaptation for Domain Generalization},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2023},
  pages     = {24172--24182}
}

@InProceedings{Ghifary15:DG-MTA,
  author = {M. Ghifary and W. Kleijn and M. Zhang and D. Balduzzi},
  booktitle = {2015 IEEE International Conference on Computer Vision (ICCV)},
  title = {Domain Generalization for Object Recognition with Multi-task Autoencoders},
  year = {2015},
  pages = {2551--2559},
  publisher = {{IEEE} Computer Society},
  address = {Los Alamitos, CA, USA}
}

@article{Cucker02:SLT,  
  title = "On the mathematical foundations of learning",  author = "Felipe Cucker and Steve Smale", 
  year = "2002",  
  volume = "39",  
  pages = "1--49",  
  journal   = "Bulletin of the American Mathematical Society",  
  publisher = "American Mathematical Society",
  number    = "1" 
}

@book{Vapnik98:SLT,
	author = {Vladimir Vapnik},
	title = {Statistical Learning Theory},
	publisher = {Wiley India Pvt Ltd},
	year = {1998}
}

@inproceedings{Yang23:Subpop-Shift,
  title={Change is Hard: A Closer Look at Subpopulation Shift},
  author={Yang, Yuzhe and Zhang, Haoran and Katabi, Dina and Ghassemi, Marzyeh},
  booktitle={International Conference on Machine Learning},
  year={2023}
}

@inproceedings{Santurkar21:Breeds,
  title={{\{}BREEDS{\}}: Benchmarks for Subpopulation Shift},
  author={Shibani Santurkar and Dimitris Tsipras and Aleksander Madry},
  booktitle={International Conference on Learning Representations},
  year={2021}
}


@inproceedings{Hardt16:Strategic,
  author = {Hardt, Moritz and Megiddo, Nimrod and Papadimitriou, Christos and Wootters, Mary},
  title = {Strategic Classification},
  year = {2016},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  booktitle = {Proceedings of the 2016 ACM Conference on Innovations in Theoretical Computer Science},
  pages = {111--122},
  numpages = {12}
}


@InProceedings{Perdomo20:Performative,
  title = {Performative Prediction},
  author = {Perdomo, Juan and Zrnic, Tijana and Mendler-D{\"u}nner, Celestine and Hardt, Moritz},
  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
  pages = {7599--7609},
  year = {2020},
  volume = {119},
  publisher = {PMLR}
}



@InProceedings{Brault19:Infinite-Task,
  title = {Infinite Task Learning in RKHSs},
  author = {Brault, Romain and Lambert, Alex and Szabo, Zoltan and Sangnier, Maxime and d'Alche-Buc, Florence},
  booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics},
  pages = {1294--1302},
  year = {2019},
  volume = {89},
  publisher = {PMLR}
}


@inproceedings{Ichiro13:Parametric-Task,
  author = {Takeuchi, Ichiro and Hongo, Tatsuya and Sugiyama, Masashi and Nakajima, Shinichi},
  booktitle = {Advances in Neural Information Processing Systems},
  publisher = {Curran Associates, Inc.},
  title = {Parametric Task Learning},
  volume = {26},
  year = {2013}
}
@inproceedings{subbaswamy2019preventing,
  title={Preventing failures due to dataset shift: Learning predictive models that transport},
  author={Subbaswamy, Adarsh and Schulam, Peter and Saria, Suchi},
  booktitle={The 22nd International Conference on Artificial Intelligence and Statistics},
  pages={3118--3127},
  year={2019},
  organization={PMLR}
}
@article{rothenhausler2021anchor,
  title={Anchor regression: Heterogeneous data meet causality},
  author={Rothenh{\"a}usler, Dominik and Meinshausen, Nicolai and B{\"u}hlmann, Peter and Peters, Jonas},
  journal={Journal of the Royal Statistical Society Series B: Statistical Methodology},
  volume={83},
  number={2},
  pages={215--246},
  year={2021},
  publisher={Oxford University Press}
}
@article{fanaee2014event,
  title={Event labeling combining ensemble detectors and background knowledge},
  author={Fanaee-T, Hadi and Gama, Joao},
  journal={Progress in Artificial Intelligence},
  volume={2},
  pages={113--127},
  year={2014},
  publisher={Springer}
}

@article{damour2020,
  title = {Underspecification {{Presents Challenges}} for {{Credibility}} in {{Modern Machine Learning}}},
  author = {D'Amour, Alexander and Heller, Katherine and Moldovan, Dan and Adlam, Ben and Alipanahi, Babak and Beutel, Alex and Chen, Christina and Deaton, Jonathan and Eisenstein, Jacob and Hoffman, Matthew D. and Hormozdiari, Farhad and Houlsby, Neil and Hou, Shaobo and Jerfel, Ghassen and Karthikesalingam, Alan and Lucic, Mario and Ma, Yian and McLean, Cory and Mincu, Diana and Mitani, Akinori and Montanari, Andrea and Nado, Zachary and Natarajan, Vivek and Nielson, Christopher and Osborne, Thomas F. and Raman, Rajiv and Ramasamy, Kim and Sayres, Rory and Schrouff, Jessica and Seneviratne, Martin and Sequeira, Shannon and Suresh, Harini and Veitch, Victor and Vladymyrov, Max and Wang, Xuezhi and Webster, Kellie and Yadlowsky, Steve and Yun, Taedong and Zhai, Xiaohua and Sculley, D.},
  year = {2020},
  month = nov,
  eprint = {2011.03395},
  doi = {2011.03395v2},
  abstract = {ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline is underspecified when it can return many predictors with equivalently strong held-out performance in the training domain. Underspecification is common in modern ML pipelines, such as those based on deep learning. Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains. This ambiguity can lead to instability and poor model behavior in practice, and is a distinct failure mode from previously identified issues arising from structural mismatch between training and deployment domains. We show that this problem appears in a wide variety of practical ML pipelines, using examples from computer vision, medical imaging, natural language processing, clinical risk prediction based on electronic health records, and medical genomics. Our results show the need to explicitly account for underspecification in modeling pipelines that are intended for real-world deployment in any domain.},
  archiveprefix = {arxiv},
  keywords = {⚠️ Invalid DOI,⛔ No DOI found,Computer vision,Distribution shift,Electronic health records,Fairness,Genomics,Identifiability,Medical imaging,Natural language processing,Spurious correlation},
  file = {/Users/sfoell/Zotero/storage/F4CWLU2M/2020_D'Amour et al.pdf}
}

@article{he2009learning,
  title={Learning from imbalanced data},
  author={He, Haibo and Garcia, Edwardo A},
  journal={IEEE Transactions on knowledge and data engineering},
  volume={21},
  number={9},
  pages={1263--1284},
  year={2009},
  publisher={Ieee}
}

@article{he2021automl,
  title={AutoML: A survey of the state-of-the-art},
  author={He, Xin and Zhao, Kaiyong and Chu, Xiaowen},
  journal={Knowledge-Based Systems},
  volume={212},
  pages={106622},
  year={2021},
  publisher={Elsevier}
}

@article{tian2023recent,
  title={Recent Advances in Stochastic Gradient Descent in Deep Learning},
  author={Tian, Yingjie and Zhang, Yuqi and Zhang, Haibin},
  journal={Mathematics},
  volume={11},
  number={3},
  pages={682},
  year={2023},
  publisher={MDPI}
}

@inproceedings{chau2022spectral_ecml,
  title={Spectral ranking with covariates},
  author={Chau, Siu Lun and Cucuringu, Mihai and Sejdinovic, Dino},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={70--86},
  year={2022},
  organization={Springer}
}

@inproceedings{Krizhevsky12:ImageNet,
 author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {{ImageNet} Classification with Deep Convolutional Neural Networks},
 volume = {25},
 year = {2012}
}

@inproceedings{Cohen16:Group,
  title={Group equivariant convolutional networks},
  author={Cohen, Taco and Welling, Max},
  booktitle={International conference on machine learning},
  pages={2990--2999},
  year={2016},
  organization={PMLR}
}

@inproceedings{Vaswani17:Attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  booktitle={Advances in Neural Information Processing Systems},
  volume={30},
  year={2017}
}

@article{HofSchSmo08,
	Author = {T. Hofmann and B. Sch\"{o}lkopf and A. J. Smola},
	Journal = {Annals of Statistics},
	Number = {3},
	Pages = {1171-1220},
	Title = {Kernel methods in machine learning},
	Volume = {36},
	Year = {2008}
}

@book{Goodfellow16:DL,
  Title                    = {Deep Learning},
  Author                   = {Ian J. Goodfellow and Yoshua Bengio and Aaron Courville},
  Publisher                = {MIT Press},
  Year                     = {2016},
  Address                  = {Cambridge, MA, USA},
  Note                     = {\url{http://www.deeplearningbook.org}}
}


@article{Lecun15:DL,
	Author = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey},
	Journal = {Nature},
	Number = {7553},
	Pages = {436--444},
	Title = {Deep learning},
	Volume = {521},
	Year = {2015}
}

@article{Ratner19:MLsys,
  author       = {Alexander Ratner and
                  Dan Alistarh and
                  Gustavo Alonso and
                  David G. Andersen and
                  Peter Bailis and
                  Sarah Bird and
                  Nicholas Carlini and
                  Bryan Catanzaro and
                  Eric S. Chung and
                  Bill Dally and
                  Jeff Dean and
                  Inderjit S. Dhillon and
                  Alexandros G. Dimakis and
                  Pradeep Dubey and
                  Charles Elkan and
                  Grigori Fursin and
                  Gregory R. Ganger and
                  Lise Getoor and
                  Phillip B. Gibbons and
                  Garth A. Gibson and
                  Joseph E. Gonzalez and
                  Justin Gottschlich and
                  Song Han and
                  Kim M. Hazelwood and
                  Furong Huang and
                  Martin Jaggi and
                  Kevin G. Jamieson and
                  Michael I. Jordan and
                  Gauri Joshi and
                  Rania Khalaf and
                  Jason Knight and
                  Jakub Kone{\v{c}}n{\'y} and
                  Tim Kraska and
                  Arun Kumar and
                  Anastasios Kyrillidis and
                  Jing Li and
                  Samuel Madden and
                  H. Brendan McMahan and
                  Erik Meijer and
                  Ioannis Mitliagkas and
                  Rajat Monga and
                  Derek Gordon Murray and
                  Dimitris S. Papailiopoulos and
                  Gennady Pekhimenko and
                  Theodoros Rekatsinas and
                  Afshin Rostamizadeh and
                  Christopher R{\'{e}} and
                  Christopher De Sa and
                  Hanie Sedghi and
                  Siddhartha Sen and
                  Virginia Smith and
                  Alex Smola and
                  Dawn Song and
                  Evan R. Sparks and
                  Ion Stoica and
                  Vivienne Sze and
                  Madeleine Udell and
                  Joaquin Vanschoren and
                  Shivaram Venkataraman and
                  Rashmi Vinayak and
                  Markus Weimer and
                  Andrew Gordon Wilson and
                  Eric P. Xing and
                  Matei Zaharia and
                  Ce Zhang and
                  Ameet Talwalkar},
  title        = {{SysML}: The New Frontier of Machine Learning Systems},
  journal      = {CoRR},
  volume       = {abs/1904.03257},
  year         = {2019},
  url          = {http://arxiv.org/abs/1904.03257},
  eprinttype    = {arXiv},
  eprint       = {1904.03257}
}

@inproceedings{Ganin2015:DA,
  title = {Unsupervised Domain Adaptation by Backpropagation},
  booktitle = {Proceedings of the 32nd {{International Conference}} on {{Machine Learning}}},
  author = {Ganin, Yaroslav and Lempitsky, Victor},
  year = {2015},
  volume = {37},
  pages = {1180--1189},
  publisher = {{PMLR}}
}

@article{Winson20:UDA,
  author = {Wilson, Garrett and Cook, Diane J.},
  title = {A Survey of Unsupervised Deep Domain Adaptation},
  year = {2020},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {11},
  number = {5},
  journal = {ACM Trans. Intell. Syst. Technol.},
  articleno = {51},
  numpages = {46}
}

@article{Zhao22:UDA,
  author={Zhao, Sicheng and Yue, Xiangyu and Zhang, Shanghang and Li, Bo and Zhao, Han and Wu, Bichen and Krishna, Ravi and Gonzalez, Joseph E. and Sangiovanni-Vincentelli, Alberto L. and Seshia, Sanjit A. and Keutzer, Kurt},
  journal={{IEEE} Transactions on Neural Networks and Learning Systems}, 
  title={A Review of Single-Source Deep Unsupervised Visual Domain Adaptation}, 
  year={2022},
  volume={33},
  number={2},
  pages={473--493}
}

@article{Wang21:DG-Review,
  author    = {Jindong Wang and
               Cuiling Lan and
               Chang Liu and
               Yidong Ouyang and
               Tao Qin},
  title     = {Generalizing to Unseen Domains: {A} Survey on Domain Generalization},
  journal   = {CoRR},
  volume    = {abs/2103.03097},
  year      = {2021},
  url       = {https://arxiv.org/abs/2103.03097}
}

@article{Zhou21:DG-Review, 
  author = {Zhou, Kaiyang and Liu, Ziwei and Qiao, Yu and Xiang, Tao and Loy, Chen Change}, 
  title = {Domain Generalization: A Survey}, 
  year = {2023}, 
  publisher = {{IEEE} Computer Society}, 
  volume = {45}, 
  number = {4}, 
  journal = {{IEEE} Transactions on Pattern Analysis and Machine Intelligence}, 
  month = {apr}, 
  pages = {4396--4415}, 
  numpages = {20} 
}

@article{Shen21:OOD-Review,
  author = {Zheyan Shen and
            Jiashuo Liu and
            Yue He and
            Xingxuan Zhang and
            Renzhe Xu and
            Han Yu and
            Peng Cui},
  title = {Towards Out-Of-Distribution Generalization: {A} Survey},
  journal = {CoRR},
  volume = {abs/2108.13624},
  year = {2021},
  eprinttype = {arXiv},
  eprint = {2108.13624}
}

@article{MGDA12,
  title = {Multiple-gradient descent algorithm (MGDA) for multiobjective optimization},
  journal = {Comptes Rendus Mathematique},
  volume = {350},
  number = {5},
  pages = {313--318},
  year = {2012},
  author = {Jean-Antoine D\'esid\'eri}
}
@article{pezeshki2021gradient,
  title={Gradient starvation: A learning proclivity in neural networks},
  author={Pezeshki, Mohammad and Kaba, Oumar and Bengio, Yoshua and Courville, Aaron C and Precup, Doina and Lajoie, Guillaume},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={1256--1272},
  year={2021}
}
@article{koyama2020out,
  title={Out-of-distribution generalization with maximal invariant predictor},
  author={Koyama, Masanori and Yamaguchi, Shoichiro},
  year={2020}
}
@inproceedings{perez2018film,
  title={Film: Visual reasoning with a general conditioning layer},
  author={Perez, Ethan and Strub, Florian and De Vries, Harm and Dumoulin, Vincent and Courville, Aaron},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={32},
  number={1},
  year={2018}
}

@inproceedings{Beede20:AI, 
  author = {Beede, Emma and Baylor, Elizabeth and Hersch, Fred and Iurchenko, Anna and Wilcox, Lauren and Ruamviboonsuk, Paisan and Vardoulakis, Laura M.}, 
  title = {A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy}, 
  year = {2020}, 
  publisher = {Association for Computing Machinery}, 
  address = {New York, NY, USA},  
  booktitle = {Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems}, 
  pages = {1–-12}, 
  numpages = {12}
}

@article{HeinzeDeml18:ICP-Nonlinear,
  title = {Invariant Causal Prediction for Nonlinear Models},
  author = {Christina Heinze-Deml and Jonas Peters and Nicolai Meinshausen},
  pages = {20170016},
  volume = {6},
  number = {2},
  journal = {Journal of Causal Inference},
  year = {2018}
}

@misc{Nastl24:Causal-Predictors,
      title={Predictors from causal features do not generalize better to new domains}, 
      author={Vivian Y. Nastl and Moritz Hardt},
      year={2024},
      eprint={2402.09891},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{grab1954tables,
  title={Tables of the expected value of 1/X for positive Bernoulli and Poisson variables},
  author={Grab, Edwin L and Savage, I Richard},
  journal={Journal of the American Statistical Association},
  volume={49},
  number={265},
  pages={169--177},
  year={1954},
  publisher={Taylor \& Francis}
}

@article{Ben-Tal10:Soft-Robust,
  author = {Ben-Tal, Aharon and Bertsimas, Dimitris and Brown, David B.},
  title = {A Soft Robust Model for Optimization Under Ambiguity},
  journal = {Operations Research},
  volume = {58},
  pages = {1220--1234},
  year = {2010}
}

@article{Artzner99:CRM,
  author = {Artzner, Philippe and Delbaen, Freddy and Eber, Jean-Marc and Heath, David},
  title = {Coherent Measures of Risk},
  journal = {Mathematical Finance},
  volume = {9},
  number = {3},
  pages = {203--228},
  year = {1999}
}


@article{utkin2007decision,
  title={Decision making under incomplete data using the imprecise Dirichlet model},
  author={Utkin, Lev V and Augustin, Th},
  journal={International Journal of Approximate Reasoning},
  volume={44},
  number={3},
  pages={322--338},
  year={2007},
  publisher={Elsevier}
}

@article{shafer1992dempster,
  title={Dempster-shafer theory},
  author={Shafer, Glenn},
  journal={Encyclopedia of artificial intelligence},
  volume={1},
  pages={330--331},
  year={1992}
}


@article{cozman2000credal,
  title={Credal networks},
  author={Cozman, Fabio G},
  journal={Artificial intelligence},
  volume={120},
  number={2},
  pages={199--233},
  year={2000},
  publisher={Elsevier}
}

@article{abellan2010ensemble,
  title={An ensemble method using credal decision trees},
  author={Abellan, Joaquin and Masegosa, Andres R},
  journal={European journal of operational research},
  volume={205},
  number={1},
  pages={218--226},
  year={2010},
  publisher={Elsevier}
}

@misc{Caprio24:CLT,
  title={Credal Learning Theory}, 
  author={Michele Caprio and Maryam Sultana and Eleni Elia and Fabio Cuzzolin},
  year={2024},
  eprint={2402.00957},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}