76 citations · 755 across the 50 of their papers we have counts for
16 papers · 1 filter
Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes
Charles Riou, Pierre Alquier, Badr-Eddine Chérief-Abdellatif
Bernstein's condition is a key assumption that guarantees fast rates in machine learning. For example, the Gibbs algorithm with prior has an excess risk in , as oppos…
PAC-Bayesian Offline Contextual Bandits With Guarantees
Otmane Sakhi, Pierre Alquier, Nicolas Chopin
This paper introduces a new principled approach for off-policy learning in contextual bandits. Unlike previous work, our approach does not derive learning principles from intractab…
User-friendly introduction to PAC-Bayes bounds
Pierre Alquier
Aggregated predictors are obtained by making a set of basic predictors vote according to some weights, that is, to some probability distribution. Randomized predictors are obtained…
Meta-strategy for Learning Tuning Parameters with Guarantees
Dimitri Meunier, Pierre Alquier
Online learning methods, like the online gradient algorithm (OGA) and exponentially weighted aggregation (EWA), often depend on tuning parameters that are difficult to set in pract…
Non-exponentially weighted aggregation: regret bounds for unbounded loss functions
Pierre Alquier
We tackle the problem of online optimization with a general, possibly unbounded, loss function. It is well known that when the loss is bounded, the exponentially weighted aggregati…
A Generalization Bound for Online Variational Inference
Badr-Eddine Chérief-Abdellatif, Pierre Alquier, Mohammad Emtiyaz Khan
Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even with model mismatch and adversa…