5 citations · 11 across the 13 of their papers we have counts for
13 papers
Learning in Structured Stackelberg Games
Maria-Florina Balcan, Kiriaki Fragkia, Keegan Harris
We initiate the study of structured Stackelberg games, a novel form of strategic interaction between a leader and a follower where contextual information can be predictive of the f…
Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information
Maria-Florina Balcan, Martino Bernasconi, Matteo Castiglioni +3
We study the problem of online learning in Stackelberg games with side information between a leader and a sequence of followers. In every round the leader observes contextual infor…
Regret Minimization in Stackelberg Games with Side Information
Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan
Algorithms for playing in Stackelberg games have been deployed in real-world domains including airport security, anti-poaching efforts, and cyber-crime prevention. However, these a…
Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration
Daniel Ngo, Keegan Harris, Anish Agarwal +2
Synthetic control methods (SCMs) are a canonical approach used to estimate treatment effects from panel data in the internet economy. We shed light on a frequently overlooked but u…
Meta-Learning Adversarial Bandit Algorithms
Mikhail Khodak, Ilya Osadchiy, Keegan Harris +4
We study online meta-learning with bandit feedback, with the goal of improving performance across multiple tasks if they are similar according to some natural similarity measure. A…
Adaptive Principal Component Regression with Applications to Panel Data
Anish Agarwal, Keegan Harris, Justin Whitehouse +1
Principal component regression (PCR) is a popular technique for fixed-design error-in-variables regression, a generalization of the linear regression setting in which the observed…
Strategic Apple Tasting
Keegan Harris, Chara Podimata, Zhiwei Steven Wu
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…
Strategyproof Decision-Making in Panel Data Settings and Beyond
Keegan Harris, Anish Agarwal, Chara Podimata +1
We consider the problem of decision-making using panel data, in which a decision-maker gets noisy, repeated measurements of multiple units (or agents). We consider a setup where th…
Meta-Learning in Games
Keegan Harris, Ioannis Anagnostides, Gabriele Farina +3
In the literature on game-theoretic equilibrium finding, focus has mainly been on solving a single game in isolation. In practice, however, strategic interactions -- ranging from r…
Meta-Learning Adversarial Bandits
Maria-Florina Balcan, Keegan Harris, Mikhail Khodak +1
We study online learning with bandit feedback across multiple tasks, with the goal of improving average performance across tasks if they are similar according to some natural task-…
Bayesian Persuasion for Algorithmic Recourse
Keegan Harris, Valerie Chen, Joon Sik Kim +3
When subjected to automated decision-making, decision subjects may strategically modify their observable features in ways they believe will maximize their chances of receiving a fa…
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses
Keegan Harris, Daniel Ngo, Logan Stapleton +2
In settings where Machine Learning (ML) algorithms automate or inform consequential decisions about people, individual decision subjects are often incentivized to strategically mod…
Stateful Strategic Regression
Keegan Harris, Hoda Heidari, Zhiwei Steven Wu
Automated decision-making tools increasingly assess individuals to determine if they qualify for high-stakes opportunities. A recent line of research investigates how strategic age…