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Out-of-distribution (OOD) generalization is a challenging machine learning problem yet highly desirable in many high-stake applications.
A set of measures of centrality based on betweenness
Linton C Freeman · 1977
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Statistical learning theory, 1998
Vladimir Vapnik · 1998
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Convex optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe · 2004
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A measure of betweenness centrality based on random walks
Mark EJ Newman · 2005
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Diffusion maps
Ronald R Coifman and Stéphane Lafon · 2006
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Robust stochastic approximation approach to stochastic programming
Arkadi Nemirovski, Anatoli Juditsky, Guanghui Lan, and Alexander Shapiro · 2009
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Distributionally robust optimization under moment uncertainty with application to data-driven problems
Erick Delage and Yinyu Ye · 2010
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Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Chen Fang, Ye Xu, and Daniel N Rockmore · 2013
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Gridded 5km ghcn-daily temperature and precipitation dataset (nclimgrid) version 1
R Vose, S Applequist, M Squires, I Durre, MJ Menne, CN Williams Jr, C Fenimore, K Gleason, and D Arndt · 2014
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Hölder–lipschitz norms and their duals on spaces with semigroups, with applications to earth mover’s distance
William Leeb and Ronald Coifman · 2016
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Stochastic gradient methods for distributionally robust optimization with f-divergences
Hongseok Namkoong and John C Duchi · 2016
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Minimizing the maximal loss: How and why
Shai Shalev-Shwartz and Yonatan Wexler · 2016
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Deeper, Broader and Artier Domain Generalization
Da Li et al · 2017
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
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Does distributionally robust supervised learning give robust classifiers?
Weihua Hu, Gang Niu, Issei Sato, and Masashi Sugiyama · 2018
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Wasserstein distributionally robust kalman filtering
Soroosh Shafieezadeh Abadeh, Viet Anh Nguyen, Daniel Kuhn, and Peyman M Mohajerin Esfahani · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Robust optimization over multiple domains
Qi Qian, Shenghuo Zhu, Jiasheng Tang, Rong Jin, Baigui Sun, and Hao Li · 2019
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Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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Distributionally robust optimization and generalization in kernel methods
In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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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 · 2021
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Deep learning for spatiotemporal modeling of urbanization
Tang Li, Jing Gao, and Xi Peng · 2021
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Heterogeneous risk minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, and Zheyan Shen · 2021
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Gradient starvation: A learning proclivity in neural networks
Mohammad Pezeshki, Oumar Kaba, Yoshua Bengio, Aaron C Courville, Doina Precup, and Guillaume Lajoie · 2021
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Matthew Staib and Stefanie Jegelka · 2019
Cited alongside, same era.
Rethinking kernel methods for node representation learning on graphs
Yu Tian, Long Zhao, Xi Peng, and Dimitris Metaxas · 2019
Cited alongside, same era.
Sen1floods11: A georeferenced dataset to train and test deep learning flood algorithms for sentinel-1
Derrick Bonafilia, Beth Tellman, Tyler Anderson, and Erica Issenberg · 2020
Cited alongside, same era.
Task-robust model-agnostic meta-learning
Liam Collins, Aryan Mokhtari, and Sanjay Shakkottai · 2020
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Out-of-distribution generalization with maximal invariant predictor
Masanori Koyama and Shoichiro Yamaguchi · 2020
Cited alongside, same era.
Large-scale methods for distributionally robust optimization
Daniel Levy, Yair Carmon, John C Duchi, and Aaron Sidford · 2020
Cited alongside, same era.
Learning to learn single domain generalization
Fengchun Qiao, Long Zhao, and Xi Peng · 2020
Cited alongside, same era.
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Uncertainty-guided model generalization to unseen domains
Fengchun Qiao and Xi Peng · 2021
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Model-based domain generalization
Alexander Robey, George J Pappas, and Hamed Hassani · 2021
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The risks of invariant risk minimization
Elan Rosenfeld, Pradeep Ravikumar, and Andrej Risteski · 2021
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Diffusion earth mover’s distance and distribution embeddings
Alexander Y Tong, Guillaume Huguet, Amine Natik, Kincaid MacDonald, Manik Kuchroo, Ronald Coifman, Guy Wolf, and Smita Krishnaswamy · 2021
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Maximum-entropy adversarial data augmentation for improved generalization and robustness
Long Zhao, Ting Liu, Xi Peng, and Dimitris Metaxas · 2021
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Out-of-domain generalization from a single source: An uncertainty quantification approach
Xi Peng, Fengchun Qiao, and Long Zhao · 2022
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Graph-relational domain adaptation
Zihao Xu, Guang-He Lee, Yuyang Wang, Hao Wang, et al · 2022
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Are data-driven explanations robust against out-of-distribution data?
Tang Li, Fengchun Qiao, Mengmeng Ma, and Xi Peng · 2023
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