title: Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound
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.
authors: Valentina Zantedeschi Paul Viallard Emilie Morvant Rémi Emonet Amaury Habrard Pascal Germain Benjamin Guedj
hIndex: Amaury Habrard 26