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Invariant Causal Prediction (Peters et al., 2016) is a technique for out-of-distribution generalization which assumes that some aspects of the data distribution vary across the training set but that the underlying causal mechanisms remain constant.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
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Robust supervised learning
J Andrew Bagnell · 2005
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Structural equation models
Kenneth A Bollen · 2005
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Robust optimization , volume 28
Aharon Ben-Tal, Laurent El Ghaoui, and Arkadi Nemirovski · 2009
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Discriminative learning under covariate shift
Steffen Bickel, Michael Brückner, and Tobias Scheffer · 2009
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Estimating high-dimensional intervention effects from observational data
Marloes H Maathuis, Markus Kalisch, Peter Bühlmann, et al · 2009
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Causality
Judea Pearl · 2009
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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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Generalizing from several related classification tasks to a new unlabeled sample
Gilles Blanchard, Gyemin Lee, and Clayton Scott · 2011
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Bayesian learning for neural networks , volume 118
Radford M Neal · 2012
Earlier work this paper cites.
Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
Earlier work this paper cites.
Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
Earlier work this paper cites.
Multi-source domain adaptation: a causal view
Kun Zhang, Mingming Gong, and Bernhard Scholkopf · 2015
Earlier work this paper cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Earlier work this paper cites.
Domain adaptation with conditional transferable components
Mingming Gong, Kun Zhang, Tongliang Liu, Dacheng Tao, Clark Glymour, and Bernhard Schölkopf · 2016
Earlier work this paper cites.
Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
Cited alongside, same era.
Learning causal structures using regression invariance
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, and Kun Zhang · 2017
Cited alongside, same era.
The complementary error function
Frank R Kschischang · 2017
Cited alongside, same era.
Elements of causal inference-foundations and learning algorithms
J Peters, D Janzing, and B Schölkopf · 2017
Cited alongside, same era.
Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
Cited alongside, same era.
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
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One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai · 2019
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Preventing failures due to dataset shift: Learning predictive models that transport
Adarsh Subbaswamy, Peter Schulam, and Suchi Saria · 2019
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On learning invariant representations for domain adaptation
Han Zhao, Remi Tachet Des Combes, Kun Zhang, and Geoffrey Gordon · 2019
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Cited alongside, same era.
Invariant causal prediction for nonlinear models
Christina Heinze-Deml, Jonas Peters, and Nicolai Meinshausen · 2018
Cited alongside, same era.
Deep domain generalization via conditional invariant adversarial networks
Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, and Dacheng Tao · 2018
Cited alongside, same era.
Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
Cited alongside, same era.
Invariant models for causal transfer learning
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, and Jonas Peters · 2018
Cited alongside, same era.
Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
Cited alongside, same era.
Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C Duchi, Vittorio Murino, and Silvio Savarese · 2018
Cited alongside, same era.
Alexis Bellot and Mihaela van der Schaar · 2020
Closest in time.
A causal framework for distribution generalization
Rune Christiansen, Niklas Pfister, Martin Emil Jakobsen, Nicola Gnecco, and Jonas Peters · 2020
Closest in time.
Domain extrapolation via regret minimization
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton · 2020
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Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Remi Le Priol, and Aaron Courville · 2020
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Domain generalization using causal matching
Divyat Mahajan, Shruti Tople, and Amit Sharma · 2020
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An investigation of why overparameterization exacerbates spurious correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang · 2020
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Risk variance penalization: From distributional robustness to causality
Chuanlong Xie, Fei Chen, Yue Liu, and Zhenguo Li · 2020
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An online learning approach to interpolation and extrapolation in domain generalization
Elan Rosenfeld, Pradeep Ravikumar, and Andrej Risteski · 2021
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How neural networks extrapolate: From feedforward to graph neural networks
Keyulu Xu, Mozhi Zhang, Jingling Li, Simon Shaolei Du, Ken-Ichi Kawarabayashi, and Stefanie Jegelka · 2021
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