[1] Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H. Falk, and Ioannis Mitliagkas. Generalizing to unseen domains via distribution matching, 2021.
[2] Kumail Alhamoud, Hasan Abed Al Kader Hammoud, Motasem Alfarra, and Bernard Ghanem. Generalizability of adversarial robustness under distribution shifts. CoRR, abs/2209.15042, 2022.
[3] Anonymous. Fairness and accuracy under domain generalization. In Submitted to The Eleventh International Conference on Learning Representations, 2023. under review.
[4] Martín Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. Invariant risk minimization. CoRR, abs/1907.02893, 2019.
[5] Pranjal Awasthi, Natalie Frank, and Mehryar Mohri. Adversarial learning guarantees for linear hypotheses and neural networks. In ICML, volume 119, pages 431-441, 2020.
[6] Peter L. Bartlett, Dylan J. Foster, and Matus Telgarsky. Spectrally-normalized margin bounds for neural networks. In NeurIPS, pages 6240-6249, 2017.
[7] Peter L. Bartlett and Shahar Mendelson. Rademacher and gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3:463-482, 2002.
[8] Pouya Bashivan, Reza Bayat, Adam Ibrahim, Kartik Ahuja, Mojtaba Faramarzi, Touraj Laleh, Blake A. Richards, and Irina Rish. Adversarial feature desensitization. In NeurIPS, pages 10665-10677, 2021.
[9] Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. A theory of learning from different domains. Mach. Learn., 79(1-2):151-175, 2010.
[10] Gilles Blanchard, Aniket Anand Deshmukh, Ürün Dogan, Gyemin Lee, and Clayton Scott. Domain generalization by marginal transfer learning. Journal of Machine Learning Research, 22:2:1-2:55, 2021.
[11] Nicholas Carlini and David A. Wagner. Towards evaluating the robustness of neural networks. In SP, pages 39-57, 2017.
[12] Francesco Croce and Matthias Hein. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In ICML, pages 2206-2216, 2020.
[13] John C. Duchi and Hongseok Namkoong. Learning models with uniform performance via distributionally robust optimization. CoRR, abs/1810.08750, 2018.
[14] Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kugelgen, Hamed Hassani, George J. Pappas, and Bernhard Scholkopf. Probable domain generalization via quantile risk minimization. CoRR, abs/2207.09944, 2022.
[15] Chen Fang, Ye Xu, and Daniel N. Rockmore. Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias. In ICCV, pages 1657-1664, 2013.
[16] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, volume 70, pages 1126–1135, 2017.
[17] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor S. Lempitsky. Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17:59:1-59:35, 2016.
[18] Qingyi Gao and Xiao Wang. Theoretical investigation of generalization bounds for adversarial learning of deep neural networks. Journal of Statistical Theory and Practice, 15:1-28, 2021.
[19] Rui Gao and Anton J. Kleywegt. Distributionally robust stochastic optimization with Wasserstein distance. Mathematics if Operations Research, 48(2):603-655, 2023.