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 …

This paper will serve as an introduction to the body of work on robust subspace recovery.
Robust subspace recovery involves finding an underlying low-dimensional subspace in a …

Federated learning (FL) enables many data owners (eg, mobile devices) to train a joint ML
model (eg, a next-word prediction classifier) without the need of sharing their private training …

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 …

As machine learning systems grow in scale, so do their training data requirements, forcing
practitioners to automate and outsource the curation of training data in order to achieve state …

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 …

In high dimensions, most machine learning methods are brittle to even a small fraction of
structured outliers. To address this, we introduce a new meta-algorithm that can take in a …

We study a recently proposed large-scale distributed learning paradigm, namely Federated
Learning, where the worker machines are end users' own devices. Statistical and …

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 …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …