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Ensuring generalization to unseen environments remains a challenge.
Fast exact multiplication by the hessian
Barak A. Pearlmutter · 1994
Earlier work this paper cites.
Causality: Models, reasoning and inference
J. Pearl · 2000
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The mnist database of handwritten digits
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Robust stochastic approximation approach to stochastic programming
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro · 2009
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Generalizing from several related classification tasks to a new unlabeled sample
G. Blanchard, Gyemin Lee, and C. Scott · 2011
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Robust solutions of optimization problems affected by uncertain probabilities
A. Ben-Tal, D. D. Hertog, A. D. Waegenaere, B. Melenberg, and G. Rennen · 2013
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Domain generalization via invariant feature representation
Krikamol Muandet, D. Balduzzi, and B. Schölkopf · 2013
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, M. Maire, Serge J. Belongie, James Hays, P. Perona, D. Ramanan, Piotr Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Deep learning face attributes in the wild
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Causal inference using invariant prediction: identification and confidence intervals
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Earlier work this paper cites.
Statistics of robust optimization: A generalized empirical likelihood approach
John C. Duchi, P. Glynn, and Hongseok Namkoong · 2016
Earlier work this paper cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, E. Ustinova, Hana Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
Earlier work this paper cites.
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Earlier work this paper cites.
Sergey Zagoruyko and Nikos Komodakis · 2016
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Second-order stochastic optimization for machine learning in linear time
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The complementary error function
Frank R Kschischang · 2017
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Deeper, broader and artier domain generalization
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, S. Gross, Francisco Massa, A. Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Z. Lin, N. Gimelshein, L. Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Shiori Sagawa, Pang Wei Koh, T. Hashimoto, and Percy Liang · 2019
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Invariant rationalization
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Invariant models for causal transfer learning
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Domain generalization by solving jigsaw puzzles
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Ishaan Gulrajani and David Lopez-Paz · 2020
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Domain extrapolation via regret minimization
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Out-of-distribution generalization via risk extrapolation (rex)
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Learning robust models using the principle of independent causal mechanisms
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Learning explanations that are hard to vary
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The risks of invariant risk minimization
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Systematic generalisation with group invariant predictions
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Gradient matching for domain generalization
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