157 citations · 713 across the 56 of their papers we have counts for
6 papers · 1 filter
Sufficient Exploration for Convex Q-learning
Fan Lu, Prashant Mehta, Sean Meyn +1
In recent years there has been a collective research effort to find new formulations of reinforcement learning that are simultaneously more efficient and more amenable to analysis.…
Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization
Gergely Neu, Nneka Okolo
We propose a new stochastic primal-dual optimization algorithm for planning in a large discounted Markov decision process with a generative model and linear function approximation.…
Proximal Point Imitation Learning
Luca Viano, Angeliki Kamoutsi, Gergely Neu +2
This work develops new algorithms with rigorous efficiency guarantees for infinite horizon imitation learning (IL) with linear function approximation without restrictive coherence…
Online Learning with Off-Policy Feedback
Germano Gabbianelli, Matteo Papini, Gergely Neu
We study the problem of online learning in adversarial bandit problems under a partial observability model called off-policy feedback. In this sequential decision making problem, t…
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits
Gergely Neu, Julia Olkhovskaya, Matteo Papini +1
We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual bandits with binary losses and adversarially-selected contexts. We adapt the information-theo…
Generalization Bounds via Convex Analysis
Gábor Lugosi, Gergely Neu
Since the celebrated works of Russo and Zou (2016,2019) and Xu and Raginsky (2017), it has been well known that the generalization error of supervised learning algorithms can be bo…