CAUSALITY FROM A DISTRIBUTIONAL ROBUSTNESS POINT OF VIEW
暂无分享,去创建一个
[1] Judea Pearl,et al. Comment: Graphical Models, Causality and Intervention , 2016 .
[2] Christina Heinze-Deml,et al. Predicting the effect of interventions using invariance principles for nonlinear models , 2016 .
[3] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[4] John C. Duchi,et al. Certifiable Distributional Robustness with Principled Adversarial Training , 2017, ArXiv.
[5] Xi Chen,et al. Wasserstein Distributional Robustness and Regularization in Statistical Learning , 2017, ArXiv.
[6] Nicolai Meinshausen,et al. Causal Dantzig: Fast inference in linear structural equation models with hidden variables under additive interventions , 2017, The Annals of Statistics.
[7] Shie Mannor,et al. Robust Regression and Lasso , 2008, IEEE Transactions on Information Theory.
[8] John C. Duchi,et al. Certifying Some Distributional Robustness with Principled Adversarial Training , 2017, ICLR.
[9] T. Richardson. Single World Intervention Graphs ( SWIGs ) : A Unification of the Counterfactual and Graphical Approaches to Causality , 2013 .
[10] John Law,et al. Robust Statistics—The Approach Based on Influence Functions , 1986 .
[11] John C. Duchi,et al. Variance-based Regularization with Convex Objectives , 2016, NIPS.
[12] Terence Tao,et al. The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.
[13] Bernhard Schölkopf,et al. Domain Adaptation under Target and Conditional Shift , 2013, ICML.
[14] Christina Heinze-Deml,et al. Conditional variance penalties and domain shift robustness , 2017, Machine Learning.
[15] N. Meinshausen,et al. Anchor regression: Heterogeneous data meet causality , 2018, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
[16] Jonas Peters,et al. Causal inference by using invariant prediction: identification and confidence intervals , 2015, 1501.01332.
[17] D. Katz. The American Statistical Association , 2000 .
[18] Werner A. Stahel,et al. Robust Statistics: The Approach Based on Influence Functions , 1987 .
[19] P. J. Huber. Robust Regression: Asymptotics, Conjectures and Monte Carlo , 1973 .
[20] J. Pearl. Graphical Models, Causality, and Intervention , 2011 .
[21] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.