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[1] Bo Li,et al. Causally Regularized Learning with Agnostic Data Selection Bias , 2017, ACM Multimedia.
[2] Bernhard Schölkopf,et al. Invariant Models for Causal Transfer Learning , 2015, J. Mach. Learn. Res..
[3] J. Pearl. Causal inference in statistics: An overview , 2009 .
[4] D. Tao,et al. Deep Domain Generalization via Conditional Invariant Adversarial Networks , 2018, ECCV.
[5] Amos Storkey,et al. When Training and Test Sets are Different: Characterising Learning Transfer , 2013 .
[6] H. Shimodaira,et al. Improving predictive inference under covariate shift by weighting the log-likelihood function , 2000 .
[7] Christopher Joseph Pal,et al. A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms , 2019, ICLR.
[8] Koby Crammer,et al. Analysis of Representations for Domain Adaptation , 2006, NIPS.
[9] Sepp Hochreiter,et al. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.
[10] Neil D. Lawrence,et al. Dataset Shift in Machine Learning , 2009 .
[11] Aleksander Madry,et al. Adversarial Examples Are Not Bugs, They Are Features , 2019, NeurIPS.
[12] Anja De Waegenaere,et al. Robust Solutions of Optimization Problems Affected by Uncertain Probabilities , 2011, Manag. Sci..
[13] Matthias Bethge,et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness , 2018, ICLR.
[14] Gang Niu,et al. Does Distributionally Robust Supervised Learning Give Robust Classifiers? , 2016, ICML.
[15] Jonas Peters,et al. Causal inference by using invariant prediction: identification and confidence intervals , 2015, 1501.01332.
[16] Daniel Kuhn,et al. Distributionally Robust Logistic Regression , 2015, NIPS.
[17] Bernhard Schölkopf,et al. Identifiability of Causal Graphs using Functional Models , 2011, UAI.
[18] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[19] R. Durrett. Probability: Theory and Examples , 1993 .
[20] David Lopez-Paz,et al. Invariant Risk Minimization , 2019, ArXiv.
[21] P. Bühlmann,et al. Invariance, Causality and Robustness , 2018, Statistical Science.
[22] Suchi Saria,et al. Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport , 2018, AISTATS.
[23] Kun Zhang,et al. On Learning Invariant Representation for Domain Adaptation , 2019, ArXiv.
[24] Neil D. Lawrence,et al. When Training and Test Sets Are Different: Characterizing Learning Transfer , 2009 .
[25] N. Meinshausen,et al. Anchor regression: Heterogeneous data meet causality , 2018, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
[26] Alex ChiChung Kot,et al. Domain Generalization with Adversarial Feature Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[27] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[28] Joris M. Mooij,et al. Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions , 2017, NeurIPS.
[29] Dacheng Tao,et al. Domain Generalization via Conditional Invariant Representations , 2018, AAAI.
[30] Qi Tian,et al. Domain-Invariant Adversarial Learning for Unsupervised Domain Adaption , 2018, ArXiv.
[31] Xin Guo,et al. On the optimality of conditional expectation as a Bregman predictor , 2005, IEEE Trans. Inf. Theory.
[32] Enrique F. Castillo,et al. Expert Systems and Probabilistic Network Models , 1996, Monographs in Computer Science.
[33] Maxime Gasse,et al. Probabilistic Graphical Model Structure Learning: Application to Multi-Label Classification. (Apprentissage de Structure de Modèles Graphiques Probabilistes: Application à la Classification Multi-Label) , 2017 .
[34] Kristina Lerman,et al. A Survey on Bias and Fairness in Machine Learning , 2019, ACM Comput. Surv..
[35] Amir Najafi,et al. Robustness to Adversarial Perturbations in Learning from Incomplete Data , 2019, NeurIPS.
[36] Tommi S. Jaakkola,et al. Invariant Rationalization , 2020, ICML.
[37] Peter J. F. Lucas,et al. Advances in Probabilistic Graphical Models , 2008 .
[38] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[39] Stefano Soatto,et al. Emergence of Invariance and Disentanglement in Deep Representations , 2017, 2018 Information Theory and Applications Workshop (ITA).
[40] Gerald B. Folland,et al. Real Analysis: Modern Techniques and Their Applications , 1984 .
[41] Rajesh Ranganath,et al. Support and Invertibility in Domain-Invariant Representations , 2019, AISTATS.