Search Results for author: Keegan Harris

Found 9 papers, 1 papers with code

Can large language models explore in-context?

no code implementations22 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.

Decision Making

Regret Minimization in Stackelberg Games with Side Information

no code implementations13 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.

Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration

no code implementations26 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.

counterfactual valid

Algorithmic Persuasion Through Simulation

no code implementations29 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.

Strategyproof Decision-Making in Panel Data Settings and Beyond

no code implementations25 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.

Decision Making Econometrics

Meta-Learning Adversarial Bandits

no code implementations27 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.

Meta-Learning Multi-Armed Bandits

Bayesian Persuasion for Algorithmic Recourse

no code implementations12 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.

Decision Making

Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses

1 code implementation12 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.

Decision Making Fairness +1

Stateful Strategic Regression

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.

Decision Making regression

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