aiqbench analysis
This research advances ensemble learning by applying disintegrated PAC-Bayesian theory to convert randomized majority vote guarantees into deterministic, self-bounding algorithms. By eliminating the need for surrogate bounds, the work provides tighter generalization certificates for a single deterministic model derived directly from weight vector spaces. These theoretical improvements offer more robust and efficient frameworks for building reliable machine learning systems without relying on stochastic approximations.
On the disintegration of the stochastic majority vote: From PAC-Bayesian bounds to a self-bounding algorithm
arXiv:2609.16803v1 Announce Type: cross 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.
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.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2609.16803 [stat.ML]
(or arXiv:2609.16803v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2609.16803 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 230 words
