254 citations · 882 across the 66 of their papers we have counts for
7 papers · 1 filter
Hypothesis Testing for High-Dimensional Multinomials: A Selective Review
Sivaraman Balakrishnan, Larry Wasserman
The statistical analysis of discrete data has been the subject of extensive statistical research dating back to the work of Pearson. In this survey we review some recently develope…
Discussion of "Data-driven confounder selection via Markov and Bayesian networks" by Jenny Häggström
Edward H. Kennedy, Sivaraman Balakrishnan
In this discussion we consider why it is important to estimate causal effect parameters well even they are not identified, propose a partially identified approach for causal infere…
Stochastic Zeroth-order Optimization in High Dimensions
Yining Wang, Simon Du, Sivaraman Balakrishnan +1
We consider the problem of optimizing a high-dimensional convex function using stochastic zeroth-order queries. Under sparsity assumptions on the gradients or function values, we p…
Low Permutation-rank Matrices: Structural Properties and Noisy Completion
Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright
We consider the problem of noisy matrix completion, in which the goal is to reconstruct a structured matrix whose entries are partially observed in noise. Standard approaches to th…
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…
Computationally Efficient Robust Estimation of Sparse Functionals
Simon S. Du, Sivaraman Balakrishnan, Aarti Singh
Many conventional statistical procedures are extremely sensitive to seemingly minor deviations from modeling assumptions. This problem is exacerbated in modern high-dimensional set…
Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates
Yining Wang, Jialei Wang, Sivaraman Balakrishnan +1
Although a majority of the theoretical literature in high-dimensional statistics has focused on settings which involve fully-observed data, settings with missing values and corrupt…