40 citations · 112 across the 5 of their papers we have counts for
12 papers
Two-Sample Testing with a Graph-Based Total Variation Integral Probability Metric
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
We consider a novel multivariate nonparametric two-sample testing problem where, under the alternative, distributions and are separated in an integral probability metric ov…
Semi-Supervised U-statistics
Ilmun Kim, Larry Wasserman, Sivaraman Balakrishnan +1
Semi-supervised datasets are ubiquitous across diverse domains where obtaining fully labeled data is costly or time-consuming. The prevalence of such datasets has consistently driv…
Conservative Inference for Counterfactuals
Sivaraman Balakrishnan, Edward Kennedy, Larry Wasserman
In causal inference, the joint law of a set of counterfactual random variables is generally not identified. We show that a conservative version of the joint law - corresponding to…
Nearly Minimax Optimal Wasserstein Conditional Independence Testing
Matey Neykov, Larry Wasserman, Ilmun Kim +1
This paper is concerned with minimax conditional independence testing. In contrast to some previous works on the topic, which use the total variation distance to separate the null…
Conditional Independence Testing for Discrete Distributions: Beyond - and -tests
Ilmun Kim, Matey Neykov, Sivaraman Balakrishnan +1
This paper is concerned with the problem of conditional independence testing for discrete data. In recent years, researchers have shed new light on this fundamental problem, emphas…
Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms
Dheeraj Baby, Saurabh Garg, Tzu-Ching Yen +3
This paper focuses on supervised and unsupervised online label shift, where the class marginals varies but the class-conditionals remain invariant. In the unsupervi…