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20152026
most citedEstimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence

59 citations · 102 across the 16 of their papers we have counts for

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8 papers · 1 filter

math.ST2025

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…

math.ST2024

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…

math.ST20222 cited

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…

math.ST20213 cited

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…

math.ST2021

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…

math.ST2018

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…