Proposes a robust sequential kernel conditional independence test via adaptive betting and truncate-and-shift calibration that reduces Type I error inflation from Model-X estimation errors while retaining power.
Journal of the Royal Statistical Society Series B: Statistical Methodology , volume =
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Sublinearly structured DNNs attain feature-learning consistency and universal approximation for hierarchically compositional functions, with popular CNNs fitting this structure on image benchmarks.
A distribution-free framework applies knockoff filtering to rewrite-based detectors to achieve finite-sample FDR control for human vs. LLM text detection.
citing papers explorer
-
Sequential Kernel-based Conditional Independence Testing via Adaptive Betting
Proposes a robust sequential kernel conditional independence test via adaptive betting and truncate-and-shift calibration that reduces Type I error inflation from Model-X estimation errors while retaining power.
-
Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions
Sublinearly structured DNNs attain feature-learning consistency and universal approximation for hierarchically compositional functions, with popular CNNs fitting this structure on image benchmarks.