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 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 …

We study online learning problems in which a decision maker has to make a sequence of
costly decisions, with the goal of maximizing their expected reward while adhering to budget …

In today's online advertising markets, a crucial requirement for an advertiser is to control her
total expenditure within a time horizon under some budget. Among various budget control …

The bandits with knapsack (BwK) framework models online decision-making problems in
which an agent makes a sequence of decisions subject to resource consumption …

Successful content moderation in online platforms relies on a human-AI collaboration
approach. A typical heuristic estimates the expected harmfulness of a post and uses fixed …

Abstract Bandits with Knapsacks (BwK), the generalization of the Multi-Armed Bandits
problem under global budget constraints, has received a lot of attention in recent years. It …

Algorithmic predictions are increasingly used to inform the allocations of goods and
interventions in the public sphere. In these domains, predictions serve as a means to an …

Abstract We consider a Multi-Armed Bandit problem with covering constraints, where the
primary goal is to ensure that each arm receives a minimum expected reward while …

This paper studies the problem of stochastic constrained contextual bandits (CCB) under
general realizability condition where the expected rewards and costs are within general …