Slide 42
Slide 42 text
Intro IWERM Learning Bounds under Dataset Shifts Evaluating Model Stability to Dataset Shift Distributionally Robust Optimization Summary References
References I
Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira, et al. Analysis of representations for domain adaptation. Advances
in neural information processing systems, 19:137, 2007.
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. A theory of
learning from different domains. Machine learning, 79(1):151–175, 2010.
Aharon Ben-Tal, Dick Den Hertog, Anja De Waegenaere, Bertrand Melenberg, and Gijs Rennen. Robust solutions of optimization
problems affected by uncertain probabilities. Management Science, 59(2):341–357, 2013.
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins.
Double/debiased machine learning for treatment and structural parameters, 2018.
Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy. Optimal transport for domain adaptation. IEEE transactions
on pattern analysis and machine intelligence, 39(9):1853–1865, 2016.
Noel Cressie and Timothy RC Read. Multinomial goodness-of-fit tests. Journal of the Royal Statistical Society: Series B (Methodological), 46
(3):440–464, 1984.
John Duchi and Hongseok Namkoong. Learning models with uniform performance via distributionally robust optimization. arXiv
preprint arXiv:1810.08750, 2018.
John Duchi, Tatsunori Hashimoto, and Hongseok Namkoong. Distributionally robust losses for latent covariate mixtures. arXiv
preprint arXiv:2007.13982, 2020.
John C Duchi, Peter W Glynn, and Hongseok Namkoong. Statistics of robust optimization: A generalized empirical likelihood
approach. Mathematics of Operations Research, 2021.
Pascal Germain, Amaury Habrard, François Laviolette, and Emilie Morvant. A pac-bayesian approach for domain adaptation with
specialization to linear classifiers. In International conference on machine learning, pages 738–746. PMLR, 2013.
Pascal Germain, Amaury Habrard, François Laviolette, and Emilie Morvant. A new pac-bayesian perspective on domain adaptation.
In International conference on machine learning, pages 859–868. PMLR, 2016.
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh. Domain adaptation: Learning bounds and algorithms. arXiv preprint
arXiv:0902.3430, 2009.
42/43