The Holdout Randomization Test for Feature Selection in Black Box Models
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David M. Blei | Victor Veitch | Raul Rabadan | Wesley Tansey | Haoran Zhang | D. Blei | Wesley Tansey | R. Rabadán | H. Zhang | Victor Veitch
[1] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[2] F. Liang,et al. Bayesian Neural Networks for Selection of Drug Sensitive Genes , 2018, Journal of the American Statistical Association.
[3] Sayan Mukherjee,et al. Bayesian Approximate Kernel Regression With Variable Selection , 2015, Journal of the American Statistical Association.
[4] Lucas Janson,et al. Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection , 2016, 1610.02351.
[5] Y. Benjamini,et al. THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .
[6] Alessandro Rinaldo,et al. Distribution-Free Predictive Inference for Regression , 2016, Journal of the American Statistical Association.
[7] Avanti Shrikumar,et al. Learning Important Features Through Propagating Activation Differences , 2017, ICML.
[8] Rajen Dinesh Shah,et al. The hardness of conditional independence testing and the generalised covariance measure , 2018, The Annals of Statistics.
[9] William Stafford Noble,et al. DeepPINK: reproducible feature selection in deep neural networks , 2018, NeurIPS.
[10] Emmanuel J. Candes,et al. Robust inference with knockoffs , 2018, The Annals of Statistics.
[11] Mihaela van der Schaar,et al. KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks , 2018, ICLR.
[12] C. Bishop. Mixture density networks , 1994 .
[13] Axel Gandy,et al. QuickMMCTest: quick multiple Monte Carlo testing , 2014, Stat. Comput..
[14] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[15] James B. Brown,et al. Iterative random forests to discover predictive and stable high-order interactions , 2017, Proceedings of the National Academy of Sciences.
[16] Cynthia Rudin,et al. Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the "Rashomon" Perspective , 2018 .
[17] Leo Breiman,et al. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .
[18] David Blei,et al. Double Empirical Bayes Testing , 2020, International statistical review = Revue internationale de statistique.
[19] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.
[20] E. Candès,et al. Deep Knockoffs , 2018, Journal of the American Statistical Association.
[21] Scott W. Linderman,et al. Dose-response modeling in high-throughput cancer drug screenings: an end-to-end approach. , 2018, Biostatistics.
[22] M Sesia,et al. Gene hunting with hidden Markov model knockoffs , 2017, Biometrika.
[23] Peter Bühlmann,et al. p-Values for High-Dimensional Regression , 2008, 0811.2177.
[24] R. Tibshirani,et al. The problem of regions , 1998 .
[25] Vladimir Vovk,et al. Conditional validity of inductive conformal predictors , 2012, Machine Learning.
[26] A. Califano,et al. Dialogue on Reverse‐Engineering Assessment and Methods , 2007, Annals of the New York Academy of Sciences.
[27] David M. Blei,et al. Black Box FDR , 2018, ICML.
[28] Sreeram Kannan,et al. Mimic and Classify : A meta-algorithm for Conditional Independence Testing , 2018, ArXiv.
[29] Leo Breiman,et al. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001, Statistical Science.
[30] Amit Dhurandhar,et al. Predicting human olfactory perception from chemical features of odor molecules , 2017, Science.
[31] L. Wasserman,et al. HIGH DIMENSIONAL VARIABLE SELECTION. , 2007, Annals of statistics.
[32] E. Candès,et al. Controlling the false discovery rate via knockoffs , 2014, 1404.5609.
[33] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[34] James Y. Zou,et al. Knockoffs for the mass: new feature importance statistics with false discovery guarantees , 2018, AISTATS.
[35] Adam A. Margolin,et al. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.
[36] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[37] Le Song,et al. Learning to Explain: An Information-Theoretic Perspective on Model Interpretation , 2018, ICML.