5 citations · 11 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
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…
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-…
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…