3 papers
stat.ML2026
Sequential Kernel-based Conditional Independence Testing via Adaptive Betting
Zheng He, Danica J. Sutherland
Testing conditional independence is fundamental yet intrinsically difficult: without additional assumptions, Type I error control is impossible in general. The "Model-X'' paradigm…
stat.ML2025
On the Hardness of Conditional Independence Testing In Practice
Zheng He, Roman Pogodin, Yazhe Li +3
Tests of conditional independence (CI) underpin a number of important problems in machine learning and statistics, from causal discovery to evaluation of predictor fairness and out…
cs.LG2025
Efficient kernelized bandit algorithms via exploration distributions
Bingshan Hu, Zheng He, Danica J. Sutherland
We consider a kernelized bandit problem with a compact arm set and a fixed but unknown reward function with a finite norm in some Reproducing Kern…