6 papers · 1 filter
ICMAPE: In-Context Multiagent Pure Exploration
Xinyi Hu, Alessio Russo, Aldo Pacchiano
In some multi-agent systems, the quantity to be optimized is not an externally specified reward but the information acquired about unknown properties of the environment as done in…
Future Information-Directed Sampling for Bayesian Nonstationary Bandits
Yichen Song, Alessio Russo, Aldo Pacchiano
Exploration--exploitation is a central trade-off in bandit learning. While classical algorithms such as upper confidence bound methods and Thompson Sampling effectively balance thi…
Non-Asymptotic Best Policy Identification Guarantees in Online Reinforcement Learning
Joseph Lazzaro, Alessio Russo, Aldo Pacchiano
In this work we study the Best Policy Identification (BPI) problem in online, tabular Reinforcement Learning. This is an active sequential hypothesis testing problem in which the l…
Bayesian Online Model Selection
Aida Afshar, Yuke Zhang, Aldo Pacchiano
Online model selection in Bayesian bandits raises a fundamental exploration challenge: When an environment instance is sampled from a prior distribution, how can we design an adapt…
In-Context Pure Exploration in Continuous Decision Spaces
Alessio Russo, Yin-Ching Lee, Ryan Welch +1
In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to identify an unknown ground-truth hypothesis…
Principled Fine-tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Reward
Dipendra Misra, Aldo Pacchiano, Ta-Chung Chi +1
We study how to fine-tune LLMs using user-edit deployment data consisting of a set of context, an agent's response, and user edits. This deployment data is naturally generated by u…