16 citations · 16 across the 2 of their papers we have counts for
5 papers · 1 filter
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…
Robust Estimation via Robust Gradient Estimation
Adarsh Prasad, Arun Sai Suggala, Sivaraman Balakrishnan +1
We provide a new computationally-efficient class of estimators for risk minimization. We show that these estimators are robust for general statistical models: in the classical Hube…