A grid-sketching technique enables ε-accurate estimation of W₂² between α-Hölder smooth distributions on (0,1)^d in time ε^{-max(2, (d+1+o(1))/(1+α))}.
Introduction to Nonparametric Estimation , pages=
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5representative citing papers
Causal effects are identifiable for categorical unobserved confounders via mixture learning and tensor decomposition, yielding consistent estimators with non-asymptotic guarantees.
Empirical Bernstein confidence intervals for kernel smoothers attain nominal coverage up to a remainder of order n to the minus 2S over 2S+1 while achieving minimax optimal widths under S-th order local smoothness.
Multiscale CMH scanning generalizes the classic test to continuous spaces, achieving consistency for conditional independence testing by conditioning on marginal order statistics without requiring large stratum sizes.
citing papers explorer
-
Optimizing Computational-Statistical Runtime for Wasserstein Distance Estimation
A grid-sketching technique enables ε-accurate estimation of W₂² between α-Hölder smooth distributions on (0,1)^d in time ε^{-max(2, (d+1+o(1))/(1+α))}.
-
Causal Inference with Categorical Unobserved Confounder via Mixture Learning
Causal effects are identifiable for categorical unobserved confounders via mixture learning and tensor decomposition, yielding consistent estimators with non-asymptotic guarantees.
-
Empirical Bernstein Confidence Intervals for Kernel Smoothers: A Safe and Sharp Way to Exhaust Assumed Smoothness
Empirical Bernstein confidence intervals for kernel smoothers attain nominal coverage up to a remainder of order n to the minus 2S over 2S+1 while achieving minimax optimal widths under S-th order local smoothness.
-
Multiscale Cochran-Mantel-Haenszel Scanning for Conditional Dependency
Multiscale CMH scanning generalizes the classic test to continuous spaces, achieving consistency for conditional independence testing by conditioning on marginal order statistics without requiring large stratum sizes.
- Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming