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More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity

Rodríguez-Gálvez, Borja ; Thobaben, Ragnar ; Skoglund, Mikael

arXiv.org, 2024

Ithaca: Cornell University Library, arXiv.org

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  • Título:
    More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity
  • Autor: Rodríguez-Gálvez, Borja ; Thobaben, Ragnar ; Skoglund, Mikael
  • Assuntos: Computer Science - Learning ; Optimization ; Parameters ; Statistics - Machine Learning
  • É parte de: arXiv.org, 2024
  • Descrição: In this paper, we present new high-probability PAC-Bayes bounds for different types of losses. Firstly, for losses with a bounded range, we recover a strengthened version of Catoni's bound that holds uniformly for all parameter values. This leads to new fast-rate and mixed-rate bounds that are interpretable and tighter than previous bounds in the literature. In particular, the fast-rate bound is equivalent to the Seeger--Langford bound. Secondly, for losses with more general tail behaviors, we introduce two new parameter-free bounds: a PAC-Bayes Chernoff analogue when the loss' cumulative generating function is bounded, and a bound when the loss' second moment is bounded. These two bounds are obtained using a new technique based on a discretization of the space of possible events for the ``in probability'' parameter optimization problem. This technique is both simpler and more general than previous approaches optimizing over a grid on the parameters' space. Finally, using a simple technique that is applicable to any existing bound, we extend all previous results to anytime-valid bounds.
  • Editor: Ithaca: Cornell University Library, arXiv.org
  • Idioma: Inglês
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