254 citations · 882 across the 66 of their papers we have counts for
27 papers · 1 filter
Testing Random Effects for Binomial Data
Lucas Kania, Larry Wasserman, Sivaraman Balakrishnan
In modern scientific research, small-scale studies with limited participants are increasingly common. However, interpreting individual outcomes can be challenging, making it standa…
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…
Causal Inference with High-dimensional Discrete Covariates
Zhenghao Zeng, Sivaraman Balakrishnan, Yanjun Han +1
When estimating causal effects from observational studies, researchers often need to adjust for many covariates to deconfound the non-causal relationship between exposure and outco…
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…
Double Cross-fit Doubly Robust Estimators: Beyond Series Regression
Alec McClean, Sivaraman Balakrishnan, Edward H. Kennedy +1
Doubly robust estimators with cross-fitting have gained popularity in causal inference due to their favorable structure-agnostic error guarantees. However, when additional structur…
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…
The Fundamental Limits of Structure-Agnostic Functional Estimation
Sivaraman Balakrishnan, Edward H. Kennedy, Larry Wasserman
Many recent developments in causal inference, and functional estimation problems more generally, have been motivated by the fact that classical one-step (first-order) debiasing met…
Median Regularity and Honest Inference
Arun Kumar Kuchibhotla, Sivaraman Balakrishnan, Larry Wasserman
We introduce a new notion of regularity of an estimator called median regularity. We prove that uniformly valid (honest) inference for a functional is possible if and only if there…
Minimax rates for heterogeneous causal effect estimation
Edward H. Kennedy, Sivaraman Balakrishnan, James M. Robins +1
Estimation of heterogeneous causal effects - i.e., how effects of policies and treatments vary across subjects - is a fundamental task in causal inference. Many methods for estimat…
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…
Minimax Optimal Regression over Sobolev Spaces via Laplacian Eigenmaps on Neighborhood Graphs
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
In this paper we study the statistical properties of Principal Components Regression with Laplacian Eigenmaps (PCR-LE), a method for nonparametric regression based on Laplacian Eig…
Plugin Estimation of Smooth Optimal Transport Maps
Tudor Manole, Sivaraman Balakrishnan, Jonathan Niles-Weed +1
We analyze a number of natural estimators for the optimal transport map between two distributions and show that they are minimax optimal. We adopt the plugin approach: our estimato…
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…
The HulC: Confidence Regions from Convex Hulls
Arun Kumar Kuchibhotla, Sivaraman Balakrishnan, Larry Wasserman
We develop and analyze the HulC, an intuitive and general method for constructing confidence sets using the convex hull of estimates constructed from subsets of the data. Unlike cl…
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…
Minimax optimality of permutation tests
Ilmun Kim, Sivaraman Balakrishnan, Larry Wasserman
Permutation tests are widely used in statistics, providing a finite-sample guarantee on the type I error rate whenever the distribution of the samples under the null hypothesis is…
Minimax Optimal Conditional Independence Testing
Matey Neykov, Sivaraman Balakrishnan, Larry Wasserman
We consider the problem of conditional independence testing of and given where and are three real random variables and is continuous. We focus on two main…
Universal Inference
Larry Wasserman, Aaditya Ramdas, Sivaraman Balakrishnan
We propose a general method for constructing hypothesis tests and confidence sets that have finite sample guarantees without regularity conditions. We refer to such procedures as "…
Statistical Guarantees for Local Spectral Clustering on Random Neighborhood Graphs
Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
We study the Personalized PageRank (PPR) algorithm, a local spectral method for clustering, which extracts clusters using locally-biased random walks around a given seed node. In c…
Minimax Confidence Intervals for the Sliced Wasserstein Distance
Tudor Manole, Sivaraman Balakrishnan, Larry Wasserman
Motivated by the growing popularity of variants of the Wasserstein distance in statistics and machine learning, we study statistical inference for the Sliced Wasserstein distance--…
Robust Nonparametric Regression under Huber's -contamination Model
Simon S. Du, Yining Wang, Sivaraman Balakrishnan +2
We consider the non-parametric regression problem under Huber's -contamination model, in which an fraction of observations are subject to arbitrary adversarial noise. We fir…
Robust Multivariate Nonparametric Tests via Projection-Averaging
Ilmun Kim, Sivaraman Balakrishnan, Larry Wasserman
In this work, we generalize the Cramér-von Mises statistic via projection-averaging to obtain a robust test for the multivariate two-sample problem. The proposed test is consistent…
Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates
Sivaraman Balakrishnan, Larry Wasserman
We consider the goodness-of-fit testing problem of distinguishing whether the data are drawn from a specified distribution, versus a composite alternative separated from the null i…