Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization BoundDownload PDF

May 21, 2021 (edited Oct 19, 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: majority vote, ensemble method, generalization bound, bound-driven algorithm, PAC-Bayes
  • 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.
  • Supplementary Material: pdf
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  • Code: https://github.com/vzantedeschi/StocMV
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