Learning in the presence of outliers is a fundamental problem in statistics. Until recently, all
known efficient unsupervised learning algorithms were very sensitive to outliers in high …

Over the past years, the emergence of intelligent networks empowered by machine learning
techniques has brought great facilitates to different aspects of human life. However, using …

A recent line of work has uncovered a new form of data poisoning: so-called backdoor
attacks. These attacks are particularly dangerous because they do not affect a network's …

Modern neural networks are typically trained in an over-parameterized regime where the
parameters of the model far exceed the size of the training data. Such neural networks in …

In this paper, we study a simple and generic framework to tackle the problem of learning
model parameters when a fraction of the training samples are corrupted. Our approach is …

This paper studies the problem of distributed stochastic optimization in an adversarial setting
where, out of $ m $ machines which allegedly compute stochastic gradients every iteration …

We study high-dimensional distribution learning in an agnostic setting where an adversary is
allowed to arbitrarily corrupt an ε-fraction of the samples. Such questions have a rich history …

We survey some of the recent advances in mean estimation and regression function
estimation. In particular, we describe sub-Gaussian mean estimators for possibly heavy …

The two-stage object pose estimation paradigm first detects semantic keypoints on the
image and then estimates the 6D pose by minimizing reprojection errors. Despite performing …

We study the relationship between adversarial robustness and differential privacy in high-
dimensional algorithmic statistics. We give the first black-box reduction from privacy to …