eprintid: 10138174
rev_number: 20
eprint_status: archive
userid: 608
dir: disk0/10/13/81/74
datestamp: 2021-11-15 12:02:20
lastmod: 2021-11-15 12:02:20
status_changed: 2021-11-15 12:02:20
type: proceedings_section
metadata_visibility: show
creators_name: Zantedeschi, V
creators_name: Viallard, P
creators_name: Morvant, E
creators_name: Emonet, R
creators_name: Habrard, A
creators_name: Germain, P
creators_name: Guedj, B
title: Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
note: This version is the version of record. For information on re-use, please refer to the publisher's terms and conditions.
abstract: We investigate a stochastic counterpart of majority votes over finite ensembles of classifiers, and study its generalization properties. While our approach holds for arbitrary distributions, we instantiate it with Dirichlet distributions: this allows for a closed-form and differentiable expression for the expected risk, which then turns the generalization bound into a tractable training objective.The resulting stochastic majority vote learning algorithm achieves state-of-the-art accuracy and benefits from (non-vacuous) tight generalization bounds, in a series of numerical experiments when compared to competing algorithms which also minimize PAC-Bayes objectives -- both with uninformed (data-independent) and informed (data-dependent) priors.
date: 2021
date_type: published
publisher: Advances in Neural Information Processing Systems (NeurIPS 2021)
official_url: https://proceedings.neurips.cc/paper/2021/hash/0415740eaa4d9decbc8da001d3fd805f-Abstract.html
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1872870
lyricists_name: Guedj, Benjamin
lyricists_id: BGUED94
actors_name: Guedj, Benjamin
actors_id: BGUED94
actors_role: owner
full_text_status: public
publication: CoRR
event_title: 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
book_title: Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021)
citation:        Zantedeschi, V;    Viallard, P;    Morvant, E;    Emonet, R;    Habrard, A;    Germain, P;    Guedj, B;      (2021)    Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound.                     In:  Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021).    Advances in Neural Information Processing Systems (NeurIPS 2021)       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10138174/7/Guedj_Learning%20Stochastic%20Majority%20Votes%20by%20Minimizing%20a%20PAC-Bayes%20Generalization%20Bound_VoR.pdf