Quantum computers have demonstrable ability to solve problems at a scale beyond brute-
force classical simulation. Interest in quantum algorithms has developed in many areas …

A central result in the foundations of quantum mechanics is the Kochen-Specker theorem. In
short, it states that quantum mechanics is in conflict with classical models in which the result …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

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 combine ideas from machine learning (ML) and operations research and management
science (OR/MS) in developing a framework, along with specific methods, for using data to …

Distributionally robust optimization is a paradigm for decision making under uncertainty
where the uncertain problem data are governed by a probability distribution that is itself …

This book provides an introduction to the mathematical and algorithmic foundations of data
science, including machine learning, high-dimensional geometry, and analysis of large …

In this paper we improve upon the running time for finding a point in a convex set given a
separation oracle. In particular, given a separation oracle for a convex set K⊂ R n that is …

“Satisfiability (SAT) related topics have attracted researchers from various disciplines: logic,
applied areas such as planning, scheduling, operations research and combinatorial …