Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

In this work, we explore the unique challenges—and opportunities—of unsupervised
federated learning (FL). We develop and analyze a one-shot federated clustering scheme …

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 analyze the performance of spectral clustering for community extraction in stochastic
block models. We show that, under mild conditions, spectral clustering applied to the …

The vast majority of theoretical results in machine learning and statistics assume that the
training data is a reliable reflection of the phenomena to be learned. Similarly, most learning …

Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly
finer granularity. Motivated by the fact that most work on hierarchical clustering was based …

We use the Sum of Squares method to develop new efficient algorithms for learning well-
separated mixtures of Gaussians and robust mean estimation, both in high dimensions, that …

We show that the popular k-means clustering algorithm (Lloyd's heuristic), used for a variety
of scientific data, can result in outcomes that are unfavorable to subgroups of data (eg …

We develop efficient algorithms for estimating low-degree moments of unknown distributions
in the presence of adversarial outliers and design a new family of convex relaxations for k …

Heterogeneous federated learning without assuming any structure is challenging due to the
conflicts among non-identical data distributions of clients. In practice, clients often comprise …