3 papers
stat.ML2025
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…
stat.ML2024
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…
cs.LG2024
Generalization bounds for mixing processes via delayed online-to-PAC conversions
Baptiste Abeles, Eugenio Clerico, Gergely Neu
We study the generalization error of statistical learning algorithms in a non-i.i.d. setting, where the training data is sampled from a stationary mixing process. We develop an ana…