Objective: To provide a human–Artificial Intelligence (AI) interaction review for Machine
Learning (ML) applications to inform how to best combine both human domain expertise and …

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 …

The increasingly tight coupling between humans and system operations in domains ranging
from intelligent infrastructure to e-commerce has led to a challenging new class of problems …

This survey provides a comprehensive overview of the landscape of crowdsourcing
research, targeted at the machine learning community. We begin with an overview of the …

We study the problem of online learning in a two-player decentralized cooperative
Stackelberg game. In each round, the leader first takes an action, followed by the follower …

We study the hidden-action principal-agent problem in an online setting. In each round, the
principal posts a contract that specifies the payment to the agent based on each outcome …

We study the causal effects of financial incentives on the quality of crowdwork. We focus on
performance-based payments (PBPs), bonus payments awarded to workers for producing …

We introduce a new model of combinatorial contracts in which a principal delegates the
execution of a costly task to an agent. To complete the task, the agent can take any subset of …

We consider the classic principal-agent model of contract theory, in which a principal
designs an outcome-dependent compensation scheme to incentivize an agent to take a …

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 …