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Real-world classification problems must contend with domain shift, the (potential) mismatch between the domain where a model is deployed and the domain(s) where the training data was gathered.
“Kernel measures of conditional dependence.”
Kenji Fukumizu, Arthur Gretton, Xiaohai Sun and Bernhard Sch\"olkopf · 2007
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“One shot learning of simple visual concepts”
Brenden Lake, Ruslan Salakhutdinov, Jason Gross and Joshua Tenenbaum · 2011
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“On causal and anticausal learning”
Bernhard Sch\"olkopf et al · 2012
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“Kernel-based conditional independence test and application in causal discovery”
Kun Zhang, Jonas Peters, Dominik Janzing and Bernhard Sch\"olkopf · 2012
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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. Rockmore · 2013
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“Domain Generalization via Invariant Feature Representation”
Krikamol Muandet, David Balduzzi and Bernhard Sch\"olkopf · 2013
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“Domain adaptation under target and conditional shift”
Kun Zhang, Bernhard Sch\"olkopf, Krikamol Muandet and Zhikun Wang · 2013
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“Causal inference by using invariant prediction: identification and confidence intervals”
Jonas Peters, Peter B\"uhlmann and Nicolai Meinshausen · 2016
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“Meta-learning with memory-augmented neural networks”
Adam Santoro et al · 2016
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“Minimax estimation of maximum mean discrepancy with radial kernels”
Ilya Tolstikhin, Bharath Sriperumbudur and Bernhard Sch\"olkopf · 2016
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“Learning to reinforcement learn”
Jane Wang et al · 2016
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“Model-agnostic meta-learning for fast adaptation of deep networks”
Chelsea Finn, Pieter Abbeel and Sergey Levine · 2017
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“Deeper, broader and artier domain generalization”
Da Li, Yongxin Yang, Yi-Zhe Song and Timothy Hospedales · 2017
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“Elements of causal inference: foundations and learning algorithms”
Jonas Peters, Dominik Janzing and Bernhard Sch\"olkopf · 2017
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“From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge”
Peter Bandi et al · 2018
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“Counterfactuals uncover the modular structure of deep generative models”
Michel Besserve, Arash Mehrjou, R\’emy Sun and Bernhard Sch\"olkopf · 2018
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“Invariant causal prediction for nonlinear models”
Christina Heinze-Deml, Jonas Peters and Nicolai Meinshausen · 2018
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“Generalization in anti-causal learning”
Niki Kilbertus, Giambattista Parascandolo and Bernhard Sch\"olkopf · 2018
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“Deep domain generalization via conditional invariant adversarial networks”
Ya Li et al · 2018
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“Domain adaptation by using causal inference to predict invariant conditional distributions”
Sara Magliacane et al · 2018
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“On first-order meta-learning algorithms”
Alex Nichol, Joshua Achiam and John Schulman · 2018
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“Invariant Models for Causal Transfer Learning”
Mateo Rojas-Carulla, Bernhard Sch\"olkopf, Richard. Turner and Jonas Peters · 2018
Cited alongside, same era.
“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 Koh et al · 2021
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“Learning causal semantic representation for out-of-distribution prediction”
Chang Liu et al · 2021
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“Nonlinear invariant risk minimization: A causal approach”
Chaochao Lu, Yuhuai Wu, Jo\’se Hern\’andez-Lobato and Bernhard Sch\"olkopf · 2021
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“Representation Learning via Invariant Causal Mechanisms”
Jovana Mitrovic et al · 2021
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“Anchor regression: Heterogeneous data meet causality”
Dominik Rothenh\"ausler, Nicolai Meinshausen, Peter B\"uhlmann and Jonas Peters · 2021
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“Prefrontal cortex as a meta-reinforcement learning system”
Jane Wang et al · 2018
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Martin Arjovsky, L\’eon Bottou, Ishaan Gulrajani and David Lopez-Paz · 2019
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Yibo Jiang and Nakul Verma · 2019
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Shiori Sagawa, Pang Koh, Tatsunori Hashimoto and Percy Liang · 2019
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“Preventing failures due to dataset shift: Learning predictive models that transport”
Adarsh Subbaswamy, Peter Schulam and Suchi Saria · 2019
Cited alongside, same era.
Haoyue Bai et al · 2020
Cited alongside, same era.
“Few-shot learning via learning the representation, provably”
Simon Du et al · 2020
Cited alongside, same era.
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“Towards Causal Representation Learning”
Bernhard Sch\"olkopf et al · 2021
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“Gradient Matching for Domain Generalization”
Yuge Shi et al · 2021
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“Provable meta-learning of linear representations”
Nilesh Tripuraneni, Chi Jin and Michael Jordan · 2021
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“Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests”
Victor Veitch, Alexander D’Amour, Steve Yadlowsky and Jacob Eisenstein · 2021
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“On calibration and out-of-domain generalization”
Yoav Wald, Amir Feder, Daniel Greenfeld and Uri Shalit · 2021
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“Generalizing to Unseen Domains: A Survey on Domain Generalization”
Jindong Wang et al · 2021
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“Desiderata for representation learning: A causal perspective”
Yixin Wang and Michael Jordan · 2021
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“Causally Invariant Predictor with Shift-Robustness”
Xiangyu Zheng, Xinwei Sun, Wei Chen and Tie-Yan Liu · 2021
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“Domain Generalization: A Survey”
Kaiyang Zhou et al · 2021
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“Causality Inspired Representation Learning for Domain Generalization”
Fangrui Lv et al · 2022
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“Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization”
Elan Rosenfeld, Pradeep Ravikumar and Andrej Risteski · 2022
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