This work considers a computationally and statistically efficient parameter estimation method
for a wide class of latent variable models—including Gaussian mixture models, hidden …

Partial observability is ubiquitous in applications of Reinforcement Learning (RL), in which
agents learn to make a sequence of decisions despite lacking complete information about …

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 …

Self-supervised learning is an empirically successful approach to unsupervised learning
based on creating artificial supervised learning problems. A popular self-supervised …

Tensor Attention, a multi-view attention that is able to capture high-order correlations among
multiple modalities, can overcome the representational limitations of classical matrix …

This paper considers the problem of designing optimal algorithms for reinforcement learning
in two-player zero-sum games. We focus on self-play algorithms which learn the optimal …

Many real-world problems involve the optimization of multiple, possibly conflicting
objectives. Multi-objective reinforcement learning (MORL) is a generalization of standard …

Hidden Markov Models (HMMs) are one of the most fundamental and widely used statistical
tools for modeling discrete time series. In general, learning HMMs from data is …

We describe a general technique that yields the first Statistical Query lower bounds for a
range of fundamental high-dimensional learning problems involving Gaussian distributions …

Mixture models are a fundamental tool in applied statistics and machine learning for treating
data taken from multiple subpopulations. The current practice for estimating the parameters …