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Domain generalization aims at performing well on unseen test environments with data from a limited number of training environments.
Decoupling inequalities for the tail probabilities of multivariate u-statistics
Victor H de la Peña and Stephen J Montgomery-Smith · 1995
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Metric entropy of the grassmann manifold
Alain Pajor · 1998
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Adaptive estimation of a quadratic functional by model selection
B. Laurent and P. Massart · 2000
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Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
Daniel A. Spielman and Shang-Hua Teng · 2004
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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Differential topology , volume 370
Victor Guillemin and Alan Pollack · 2010
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Matrices: Theory and Applications
D. Serre · 2010
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Generalizing from several related classification tasks to a new unlabeled sample
Gilles Blanchard, Gyemin Lee, and Clayton Scott · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Introduction to the non-asymptotic analysis of random matrices
R. Vershynin · 2012
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Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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Hanson-Wright inequality and sub-gaussian concentration
Mark Rudelson and Roman Vershynin · 2013
Cited alongside, same era.
Learning transferable features with deep adaptation networks
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan · 2015
Cited alongside, same era.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Cited alongside, same era.
Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
Cited alongside, same era.
Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
Cited alongside, same era.
Label efficient learning of transferable representations acrosss domains and tasks
Zelun Luo, Yuliang Zou, Judy Hoffman, and Li F Fei-Fei · 2017
On learning invariant representations for domain adaptation
Han Zhao, Remi Tachet Des Combes, Kun Zhang, and Geoffrey Gordon · 2019
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Generalization and invariances in the presence of unobserved confounding
Alexis Bellot and Mihaela van der Schaar · 2020
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Advancing medical imaging informatics by deep learning-based domain adaptation
Anirudh Choudhary, Li Tong, Yuanda Zhu, and May D Wang · 2020
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Domain extrapolation via regret minimization
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2020
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Cited alongside, same era.
Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
Cited alongside, same era.
Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot · 2018
Cited alongside, same era.
Conditional adversarial domain adaptation
Mingsheng Long, ZHANGJIE CAO, Jianmin Wang, and Michael I Jordan · 2018
Cited alongside, same era.
Generalizing to unseen domains via distribution matching
Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H. Falk, and Ioannis Mitliagkas · 2019
Cited alongside, same era.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
Cited alongside, same era.
Domain randomization and pyramid consistency: Simulation-to-real generalization without accessing target domain data
Xiangyu Yue, Yang Zhang, Sicheng Zhao, Alberto Sangiovanni-Vincentelli, Kurt Keutzer, and Boqing Gong · 2019
Cited alongside, same era.
Later among the works it cites.
Domain generalization using causal matching
Divyat Mahajan, Shruti Tople, and Amit Sharma · 2020
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Domain adaptation with conditional distribution matching and generalized label shift
Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, and Geoffrey J Gordon · 2020
Later among the works it cites.
Risk variance penalization: From distributional robustness to causality
Chuanlong Xie, Fei Chen, Yue Liu, and Zhenguo Li · 2020
Later among the works it cites.
Empirical or invariant risk minimization? a sample complexity perspective
Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, and Kush R. Varshney · 2021
Closest in time.
Linear unit-tests for invariance discovery
Benjamin Aubin, Agnieszka Słowik, Martin Arjovsky, Leon Bottou, and David Lopez-Paz · 2021
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2021
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Does invariant risk minimization capture invariance?
Pritish Kamath, Akilesh Tangella, Danica Sutherland, and Nathan Srebro · 2021
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