157 citations · 713 across the 56 of their papers we have counts for
6 papers · 1 filter
Adversarial Contextual Bandits Go Kernelized
Gergely Neu, Julia Olkhovskaya, Sattar Vakili
We study a generalization of the problem of online learning in adversarial linear contextual bandits by incorporating loss functions that belong to a reproducing kernel Hilbert spa…
Importance-Weighted Offline Learning Done Right
Germano Gabbianelli, Gergely Neu, Matteo Papini
We study the problem of offline policy optimization in stochastic contextual bandit problems, where the goal is to learn a near-optimal policy based on a dataset of decision data c…
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits
Julia Olkhovskaya, Jack Mayo, Tim van Erven +2
We consider the adversarial linear contextual bandit setting, which allows for the loss functions associated with each of arms to change over time without restriction. Assuming…
Offline Primal-Dual Reinforcement Learning for Linear MDPs
Germano Gabbianelli, Gergely Neu, Nneka Okolo +1
Offline Reinforcement Learning (RL) aims to learn a near-optimal policy from a fixed dataset of transitions collected by another policy. This problem has attracted a lot of attenti…
Online-to-PAC Conversions: Generalization Bounds via Regret Analysis
Gábor Lugosi, Gergely Neu
We present a new framework for deriving bounds on the generalization bound of statistical learning algorithms from the perspective of online learning. Specifically, we construct an…
Optimistic Planning by Regularized Dynamic Programming
Antoine Moulin, Gergely Neu
We propose a new method for optimistic planning in infinite-horizon discounted Markov decision processes based on the idea of adding regularization to the updates of an otherwise s…