Testing Conditional Independence in Supervised Learning Algorithms

28 January 2019

Papers citing "Testing Conditional Independence in Supervised Learning Algorithms"

24 / 24 papers shown

Title
Knoop: Practical Enhancement of Knockoff with Over-Parameterization for Variable Selection Xiaochen Zhang Yunfeng Cai Haoyi Xiong 74 0 0 28 Jan 2025
Normalizing Flows for Knockoff-free Controlled Feature Selection Derek Hansen Brian Manzo Jeffrey Regier OOD 54 5 0 03 Jun 2021
Reasoning About Generalization via Conditional Mutual Information Thomas Steinke Lydia Zakynthinou 108 162 0 24 Jan 2020
A Primer on PAC-Bayesian Learning Benjamin Guedj 104 221 0 16 Jan 2019
Deep Knockoffs Yaniv Romano Matteo Sesia Emmanuel J. Candès BDL 56 139 0 16 Nov 2018
The conditional permutation test for independence while controlling for confounders Thomas B. Berrett Yi Wang Rina Foygel Barber R. Samworth 68 16 0 14 Jul 2018
The Hardness of Conditional Independence Testing and the Generalised Covariance Measure Rajen Dinesh Shah J. Peters 94 293 0 19 Apr 2018
Fast Conditional Independence Test for Vector Variables with Large Sample Sizes Krzysztof Chalupka Pietro Perona F. Eberhardt VLM 36 36 0 08 Apr 2018
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 91 2,332 0 01 Nov 2017
Gene Hunting with Knockoffs for Hidden Markov Models Matteo Sesia C. Sabatti Emmanuel J. Candès 36 133 0 14 Jun 2017
On Calibration of Modern Neural Networks Chuan Guo Geoff Pleiss Yu Sun Kilian Q. Weinberger UQCV 228 5,774 0 14 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 744 21,613 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 150 3,848 0 10 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 151 5,920 0 04 Mar 2017
Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery Eric V. Strobl Kun Zhang Shyam Visweswaran BDL 58 174 0 13 Feb 2017
Bootstrapping and Sample Splitting For High-Dimensional, Assumption-Free Inference Alessandro Rinaldo Larry A. Wasserman M. G'Sell Jing Lei 53 94 0 16 Nov 2016
PAC-Bayesian Theory Meets Bayesian Inference Pascal Germain Francis R. Bach Alexandre Lacoste Simon Lacoste-Julien 60 182 0 27 May 2016
Distribution-Free Predictive Inference For Regression Jing Lei M. G'Sell Alessandro Rinaldo Robert Tibshirani Larry A. Wasserman 263 832 0 14 Apr 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 798 16,828 0 16 Feb 2016
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R Marvin N. Wright A. Ziegler 228 2,760 0 18 Aug 2015
Controlling the false discovery rate via knockoffs Rina Foygel Barber Emmanuel J. Candès 181 746 0 22 Apr 2014
A Scalable Conditional Independence Test for Nonlinear, Non-Gaussian Data Joseph Ramsey 47 51 0 20 Jan 2014
Kernel-based Conditional Independence Test and Application in Causal Discovery Kun Zhang J. Peters Dominik Janzing Bernhard Schölkopf BDL CML 82 618 0 14 Feb 2012
Learning Bayesian Networks with the bnlearn R Package M. Scutari BDL 153 1,723 0 26 Aug 2009