Testing Conditional Independence in Supervised Learning Algorithms

28 January 2019

Papers citing "Testing Conditional Independence in Supervised Learning Algorithms"

8 / 8 papers shown

Title
What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Jan Kapar Niklas Koenen Martin Jullum 64 0 0 29 Apr 2025
Knoop: Practical Enhancement of Knockoff with Over-Parameterization for Variable Selection Xiaochen Zhang Yunfeng Cai Haoyi Xiong 42 0 0 28 Jan 2025
Variable Importance in High-Dimensional Settings Requires Grouping Ahmad Chamma Bertrand Thirion D. Engemann 29 3 0 18 Dec 2023
Conditional Feature Importance for Mixed Data Kristin Blesch David S. Watson Marvin N. Wright 40 7 0 06 Oct 2022
A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data Ankur Ankan J. Textor CML 18 5 0 09 Jun 2022
Model-Agnostic Confidence Intervals for Feature Importance: A Fast and Powerful Approach Using Minipatch Ensembles Luqin Gan Lili Zheng Genevera I. Allen 18 6 0 05 Jun 2022
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 10 397 0 19 Oct 2020
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R Marvin N. Wright A. Ziegler 93 2,729 0 18 Aug 2015