no code implementations • 22 Mar 2024 • Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang, Aleksandrs Slivkins
We investigate the extent to which contemporary Large Language Models (LLMs) can engage in exploration, a core capability in reinforcement learning and decision making.
no code implementations • 13 Feb 2024 • Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan
Stackelberg games are perhaps one of the biggest success stories of algorithmic game theory over the last decade, as algorithms for playing in Stackelberg games have been deployed in many real-world domains including airport security, anti-poaching efforts, and cyber-crime prevention.
no code implementations • 26 Dec 2023 • Daniel Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu
We consider the setting of synthetic control methods (SCMs), a canonical approach used to estimate the treatment effect on the treated in a panel data setting.
no code implementations • 29 Nov 2023 • Keegan Harris, Nicole Immorlica, Brendan Lucier, Aleksandrs Slivkins
After a fixed number of queries, the sender commits to a messaging policy and the receiver takes the action that maximizes her expected utility given the message she receives.
no code implementations • 25 Nov 2022 • Keegan Harris, Anish Agarwal, Chara Podimata, Zhiwei Steven Wu
Unlike this classical setting, we permit the units generating the panel data to be strategic, i. e. units may modify their pre-intervention outcomes in order to receive a more desirable intervention.
no code implementations • 27 May 2022 • Maria-Florina Balcan, Keegan Harris, Mikhail Khodak, Zhiwei Steven Wu
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-similarity measure.
no code implementations • 12 Dec 2021 • Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu
While the decision maker's problem of finding the optimal Bayesian incentive-compatible (BIC) signaling policy takes the form of optimization over infinitely-many variables, we show that this optimization can be cast as a linear program over finitely-many regions of the space of possible assessment rules.
1 code implementation • 12 Jul 2021 • Keegan Harris, Daniel Ngo, Logan Stapleton, Hoda Heidari, Zhiwei Steven Wu
In settings where Machine Learning (ML) algorithms automate or inform consequential decisions about people, individual decision subjects are often incentivized to strategically modify their observable attributes to receive more favorable predictions.
no code implementations • NeurIPS 2021 • Keegan Harris, Hoda Heidari, Zhiwei Steven Wu
In particular, we consider settings in which the agent's effort investment today can accumulate over time in the form of an internal state - impacting both his future rewards and that of the principal.