254 citations · 882 across the 66 of their papers we have counts for
11 papers · 1 filter
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…
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…
Heavy-tailed Streaming Statistical Estimation
Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan +1
We consider the task of heavy-tailed statistical estimation given streaming -dimensional samples. This could also be viewed as stochastic optimization under heavy-tailed distrib…
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…
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…
Gaussian Universal Likelihood Ratio Testing
Robin Dunn, Aaditya Ramdas, Sivaraman Balakrishnan +1
The classical likelihood ratio test (LRT) based on the asymptotic chi-squared distribution of the log likelihood is one of the fundamental tools of statistical inference. A recent…
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…
On Proximal Policy Optimization's Heavy-tailed Gradients
Saurabh Garg, Joshua Zhanson, Emilio Parisotto +6
Modern policy gradient algorithms such as Proximal Policy Optimization (PPO) rely on an arsenal of heuristics, including loss clipping and gradient clipping, to ensure successful l…