59 citations · 102 across the 13 of their papers we have counts for
28 papers
Local permutation tests for conditional independence
Ilmun Kim, Matey Neykov, Sivaraman Balakrishnan +1
In this paper, we investigate local permutation tests for testing conditional independence between two random vectors and given . The local permutation test determines t…
Mixture Proportion Estimation and PU Learning: A Modern Approach
Saurabh Garg, Yifan Wu, Alex Smola +2
Given only positive examples and unlabeled examples (from both positive and negative classes), we might hope nevertheless to estimate an accurate positive-versus-negative classifie…
Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
In this paper we study the statistical properties of Laplacian smoothing, a graph-based approach to nonparametric regression. Under standard regularity conditions, we establish upp…
RATT: Leveraging Unlabeled Data to Guarantee Generalization
Saurabh Garg, Sivaraman Balakrishnan, J. Zico Kolter +1
To assess generalization, machine learning scientists typically either (i) bound the generalization gap and then (after training) plug in the empirical risk to obtain a bound on th…
Minimax Optimal Conditional Density Estimation under Total Variation Smoothness
Michael Li, Matey Neykov, Sivaraman Balakrishnan
This paper studies the minimax rate of nonparametric conditional density estimation under a weighted absolute value loss function in a multivariate setting. We first demonstrate th…
Semiparametric counterfactual density estimation
Edward H. Kennedy, Sivaraman Balakrishnan, Larry Wasserman
Causal effects are often characterized with averages, which can give an incomplete picture of the underlying counterfactual distributions. Here we consider estimating the entire co…