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 study an interesting variant of the stochastic multi-armed bandit problem, which we call
the Fair-MAB problem, where, in addition to the objective of maximizing the sum of expected …

Learning from repeated play in a fixed two-player zero-sum game is a classic problem in
game theory and online learning. We consider a variant of this problem where the game …

We study online learning problems in which a decision maker has to take a sequence of
decisions subject to $ m $ long-term constraints. The goal of the decision maker is to …

Bilateral trade models the problem of intermediating between two rational agents—a seller
and a buyer—both characterized by a private valuation for an item they want to trade. We …

We study the problem of $ K $-armed dueling bandit for both stochastic and adversarial
environments, where the goal of the learner is to aggregate information through relative …

Large-scale, two-sided matching platforms must find market outcomes that align with user
preferences while simultaneously learning these preferences from data. But since …

Budget management strategies in repeated auctions have received growing attention in
online advertising markets. However, previous work on budget management in online …

We study a game between autobidding algorithms that compete in an online advertising
platform. Each autobidder is tasked with maximizing its advertiser's total value over multiple …

Contextual bandit algorithms are applied in a wide range of domains, from advertising to
recommender systems, from clinical trials to education. In many of these domains, malicious …