Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

We introduce and study a partial-information model of online learning, where a decision
maker repeatedly chooses from a finite set of actions and observes some subset of the …

Inspired by applications in pricing and contract design, we study the maximization of one-
sided Lipschitz functions, which only provide the (weaker) guarantee that they do not grow …

When an AI system interacts with multiple users, it frequently needs to make allocation
decisions. For instance, a virtual agent decides whom to pay attention to in a group, or a …

We study contextual bandit learning with an abstract policy class and continuous action
space. We obtain two qualitatively different regret bounds: one competes with a smoothed …

First-price auctions have very recently swept the online advertising industry, replacing
second-price auctions as the predominant auction mechanism on many platforms. This shift …

We study online learning in repeated first-price auctions where a bidder, only observing the
winning bid at the end of each auction, learns to adaptively bid to maximize the cumulative …

We derive generalization and excess risk bounds for neural networks using a family of
complexity measures based on a multilevel relative entropy. The bounds are obtained by …

We investigate the problem of learning a Lipschitz function from binary feedback. In this
problem, a learner is trying to learn a Lipschitz function $ f:[0, 1]^ d\rightarrow [0, 1] $ over …

We create a computationally tractable learning algorithm for contextual bandits with
continuous actions having unknown structure. The new reduction-style algorithm composes …