Inc., 2019. URL https://proceedings.neurips.cc/paper_files/paper/2019/file/2974788b53f73e7950e8aa49f3a306db-Paper.pdf.
[13] Rasool Fakoor, Pratik Chaudhari, Jonas Mueller, and Alexander J. Smola. Trade: Transformers for density estimation, 2020. URL https://arxiv.org/abs/2004.02441.
[14] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. Domain-adversarial training of neural networks, 2016.
[15] J. Ghosh and A. Nag. An Overview of Radial Basis Function Networks, pages 1-36. Physica-Verlag HD, Heidelberg, 2001. ISBN 978-3-7908-1826-0. doi: 10.1007/978-3-7908-1826-0_1. URL https://doi.org/10.1007/978-3-7908-1826-0_1.
[16] Ishaan Gulrajani and David Lopez-Paz. In search of lost domain generalization, 2020.
[17] David Gutman, Noel CF Codella, Emre Celebi, Brian Helba, Michael Marchetti, Nabin Mishra, and Allan Halpern. Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (isbi) 2016, hosted by the international skin imaging collaboration (isic). Eprint arXiv:1605.01397, 2016.
[18] Christina Heinze-Deml and Nicolai Meinshausen. Conditional variance penalties and domain shift robustness, 2019.
[19] Andrew Jaegle, Felix Gimeno, Andrew Brock, Andrew Zisserman, Oriol Vinyals, and Joao Carreira. Perceiver: General perception with iterative attention, 2021. URL https://arxiv.org/abs/2103.03206.
[20] Yibo Jiang and Victor Veitch. Invariant and transportable representations for anti-causal domain shifts, 2022. URL https://arxiv.org/abs/2207.01603.
[21] Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, and Ricardo Silva. Causal machine learning: A survey and open problems, 2022. URL https://arxiv.org/abs/2206.15475.
[22] Pritish Kamath, Akilesh Tangella, Danica J. Sutherland, and Nathan Srebro. Does invariant risk minimization capture invariance?, 2021.
[23] Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang. Wilds: A benchmark of in-the-wild distribution shifts, 2021.
[24] Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, and Yarin Gal. Self-attention between datapoints: Going beyond individual input-output pairs in deep learning, 2021. URL https://arxiv.org/abs/2106.02584.
[25] Nikita Kotelevskii, Aleksandr Artemenkov, Kirill Fedyanin, Fedor Noskov, Alexander Fishkov, Artem Shelmanov, Artem Vazhentsev, Aleksandr Petiushko, and Maxim Panov. Nonparametric uncertainty quantification for single deterministic neural network, 2022.
[26] Masanori Koyama and Shoichiro Yamaguchi. When is invariance useful in an out-of-distribution generalization problem?, 2021.
[27] David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville. Out-of-distribution generalization via risk extrapolation (rex), 2021.
[28] Aviral Kumar, Sunita Sarawagi, and Ujjwal Jain. Trainable calibration measures for neural networks from kernel mean embeddings. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 2805-2814. PMLR, 10-15 Jul 2018. URL https://proceedings.mlr.press/v80/kumar18a.html.