157 citations · 713 across the 56 of their papers we have counts for
8 papers · 1 filter
Online learning with Erdős-Rényi side-observation graphs
Tomáš Kocák, Gergely Neu, Michal Valko
We consider adversarial multi-armed bandit problems where the learner is allowed to observe losses of a number of arms beside the arm that it actually chose. We study the case wher…
Linear Bandits with Non-i.i.d. Noise
Baptiste Abélès, Eugenio Clerico, Hamish Flynn +1
We study the linear stochastic bandit problem, relaxing the standard i.i.d. assumption on the observation noise. As an alternative to this restrictive assumption, we allow the nois…
Online-to-PAC generalization bounds under graph-mixing dependencies
Baptiste Abélès, Eugenio Clerico, Gergely Neu
Traditional generalization results in statistical learning require a training data set made of independently drawn examples. Most of the recent efforts to relax this independence a…
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…
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…
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…