Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

We present a novel approach to federated learning that endows its aggregation process with
greater robustness to potential poisoning of local data or model parameters of participating …

Semidefinite programs (SDPs) are a fundamental class of optimization problems with
important recent applications in approximation algorithms, quantum complexity, robust …

We propose Byzantine-robust federated learning protocols with nearly optimal statistical
rates based on recent progress in high dimensional robust statistics. In contrast to prior work …

In statistical learning and analysis from shared data, which is increasingly widely adopted in
platforms such as federated learning and meta-learning, there are two major concerns …

We study two problems in high-dimensional robust statistics:\emph {robust mean estimation}
and\emph {outlier detection}. In robust mean estimation the goal is to estimate the mean …

We construct an algorithm for estimating the mean of a heavy-tailed random variable when
given an adversarial corrupted sample of N independent observations. The only assumption …

This paper introduces a new robust interior point method analysis for semidefinite
programming (SDP). This new robust analysis can be combined with either logarithmic …

We study the problem of estimating the mean of a distribution in high dimensions when
either the samples are adversarially corrupted or the distribution is heavy-tailed. Recent …

List-decodable Linear Regression Page 1 List-decodeable Linear Regression Sushrut
Karmalkar University of Texas at Austin sushrutk@cs.utexas.edu Adam R. Klivans University of …