29 citations · 61 across the 6 of their papers we have counts for
8 papers
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…
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…
Robust Linear Regression: Optimal Rates in Polynomial Time
Ainesh Bakshi, Adarsh Prasad
We obtain robust and computationally efficient estimators for learning several linear models that achieve statistically optimal convergence rate under minimal distributional assump…
Learning Minimax Estimators via Online Learning
Kartik Gupta, Arun Sai Suggala, Adarsh Prasad +2
We consider the problem of designing minimax estimators for estimating the parameters of a probability distribution. Unlike classical approaches such as the MLE and minimum distanc…
A Unified Approach to Robust Mean Estimation
Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
In this paper, we develop connections between two seemingly disparate, but central, models in robust statistics: Huber's epsilon-contamination model and the heavy-tailed noise mode…
Revisiting Adversarial Risk
Arun Sai Suggala, Adarsh Prasad, Vaishnavh Nagarajan +1
Recent works on adversarial perturbations show that there is an inherent trade-off between standard test accuracy and adversarial accuracy. Specifically, they show that no classifi…