On the disintegration of the stochastic majority vote: From PAC-Bayesian bounds to a self-bounding algorithm

2026-09-15

AI Summary

本文解决了随机多数投票的确定性转化问题，通过应用最新的解构PAC-Bayesian理论直接到权重向量空间，提出了一个自我约束的学习算法。

Abstract

Weighted majority votes are central to many successful ensemble methods. PAC-Bayesian theory provides tight generalization guarantees for such models by analyzing the expected risk of stochastic classifiers, while analyzing the risk of deterministic majority votes relies on surrogate bounds. To avoid these surrogates, Zantedeschi et al. ( 2021) introduced guarantees for stochastic majority votes, but the resulting models remain randomized. In this paper, we propose a derandomization framework for stochastic majority votes. To do so, we apply recent advances in disintegrated PAC-Bayesian theory directly to the space of majority vote weight vectors, transforming stochastic guarantees into certificates for a single deterministic majority vote. We derive two families of high-probability generalization bounds, covering both data-independent and data-dependent constructions of the ensemble, which naturally lead to a self-bounding learning algorithm optimizing deterministic majority vote guarantees.

Problem

• stochastic majority votes
• derandomization
• PAC-Bayesian theory
• generalization bounds

Innovation

• derandomization framework
• disintegrated PAC-Bayesian theory
• deterministic majority vote
• generalization bounds
• self-bounding learning algorithm