activity
20142021
most citedRisk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm

85 citations · 100 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2021★ 12 cited

SIGIR 2021 E-Commerce Workshop Data Challenge

Jacopo Tagliabue, Ciro Greco, Jean-Francis Roy +4

The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations". The challenge addresses the growing ne…

cs.LG2015

Efficient Learning of Ensembles with QuadBoost

Louis Fortier-Dubois, François Laviolette, Mario Marchand +2

We first present a general risk bound for ensembles that depends on the Lp norm of the weighted combination of voters which can be selected from a continuous set. We then propose a…

stat.ML2015★ 85 cited

Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm

Pascal Germain, Alexandre Lacasse, François Laviolette +2

We propose an extensive analysis of the behavior of majority votes in binary classification. In particular, we introduce a risk bound for majority votes, called the C-bound, that t…

stat.ML2015★ 3 cited

On Generalizing the C-Bound to the Multiclass and Multi-label Settings

Francois Laviolette, Emilie Morvant, Liva Ralaivola +1

The C-bound, introduced in Lacasse et al., gives a tight upper bound on the risk of a binary majority vote classifier. In this work, we present a first step towards extending this…

stat.ML2014

On the Generalization of the C-Bound to Structured Output Ensemble Methods

François Laviolette, Emilie Morvant, Liva Ralaivola +1

This paper generalizes an important result from the PAC-Bayesian literature for binary classification to the case of ensemble methods for structured outputs. We prove a generic ver…