18 citations · 74 across the 14 of their papers we have counts for
8 papers · 1 filter
Byzantine-Resilient Non-Convex Stochastic Gradient Descent
Zeyuan Allen-Zhu, Faeze Ebrahimian, Jerry Li +1
We study adversary-resilient stochastic distributed optimization, in which machines can independently compute stochastic gradients, and cooperate to jointly optimize over their…
List-Decodable Mean Estimation in Nearly-PCA Time
Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard +2
Traditionally, robust statistics has focused on designing estimators tolerant to a minority of contaminated data. Robust list-decodable learning focuses on the more challenging reg…
Security and Machine Learning in the Real World
Ivan Evtimov, Weidong Cui, Ece Kamar +3
Machine learning (ML) models deployed in many safety- and business-critical systems are vulnerable to exploitation through adversarial examples. A large body of academic research h…
Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization
Samuel B. Hopkins, Jerry Li, Fred Zhang
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 dev…
Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time
Jerry Li, Guanghao Ye
Robust covariance estimation is the following, well-studied problem in high dimensional statistics: given samples from a -dimensional Gaussian $\mathcal{N}(\boldsymbol{0}, Σ…
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing
Arun Jambulapati, Jerry Li, Kevin Tian
We develop two methods for the following fundamental statistical task: given an -corrupted set of samples from a -dimensional sub-Gaussian distribution, return an approxi…