Strategic classification studies learning in settings where self-interested users can
strategically modify their features to obtain favorable predictive outcomes. A key working …

Abstract In settings where Machine Learning (ML) algorithms automate or inform
consequential decisions about people, individual decision subjects are often incentivized to …

Algorithmic decision-making in high-stakes domains often involves assigning decisions to
agents with incentives to strategically modify their input to the algorithm. In addition to …

In this work, we consider classification of agents who can both game and improve. For
example, people wishing to get a loan may be able to take some actions that increase their …

A broad current application of algorithms is in formal and quantitative measures of murky
concepts–like merit–to make decisions. When people strategically respond to these sorts of …

We study offline reinforcement learning under a novel model called strategic MDP, which
characterizes the strategic interactions between a principal and a sequence of myopic …

The primary goal in recommendation is to suggest relevant content to users, but optimizing
for accuracy often results in recommendations that lack diversity. To remedy this …

We initiate the study of strategic behavior in screening processes with multiple classifiers.
We focus on two contrasting settings: a" conjunctive” setting in which an individual must …

This paper introduces\textit {online bilevel optimization} in which a sequence of time-varying
bilevel problems is revealed one after the other. We extend the known regret bounds for …

The success of machine learning across a wide array of tasks and applications has made it
appealing to use it also in the social domain. Indeed, learned models now form the …