Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Harris, Keegan"'
We investigate the extent to which contemporary Large Language Models (LLMs) can engage in exploration, a core capability in reinforcement learning and decision making. We focus on native performance of existing LLMs, without training interventions.
Externí odkaz:
http://arxiv.org/abs/2403.15371
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 algorithms often fail to take into consideration the additional informa
Externí odkaz:
http://arxiv.org/abs/2402.08576
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. We shed light on a frequently overlooked but ubiquitous assumption made in SCMs of "overla
Externí odkaz:
http://arxiv.org/abs/2312.16307
We study a Bayesian persuasion game where a sender wants to persuade a receiver to take a binary action, such as purchasing a product. The sender is informed about the (binary) state of the world, such as whether the quality of the product is high or
Externí odkaz:
http://arxiv.org/abs/2311.18138
Autor:
Khodak, Mikhail, Osadchiy, Ilya, Harris, Keegan, Balcan, Maria-Florina, Levy, Kfir Y., Meir, Ron, Wu, Zhiwei Steven
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. As the first to target the adversarial online-within-online partial-inf
Externí odkaz:
http://arxiv.org/abs/2307.02295
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 covariates are corrupted with random noise. We provide the first time-
Externí odkaz:
http://arxiv.org/abs/2307.01357
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 dealing with incentives, in many domains of interest (e.g. lending an
Externí odkaz:
http://arxiv.org/abs/2306.06250
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 there is a pre-intervention period, when the principal observes the outc
Externí odkaz:
http://arxiv.org/abs/2211.14236
Autor:
Harris, Keegan, Anagnostides, Ioannis, Farina, Gabriele, Khodak, Mikhail, Wu, Zhiwei Steven, Sandholm, Tuomas
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 routing problems to online advertising auctions -- evolve dynamically,
Externí odkaz:
http://arxiv.org/abs/2209.14110
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. As the first to target the adversarial setting, we
Externí odkaz:
http://arxiv.org/abs/2205.14128