Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry

Wiese, Jonas Gregor; Wimmer, Lisa; Papamarkou, Theodore; Bischl, Bernd ; Günnemann, Stephan und Rügamer, David (2023): Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Turin, Italy, 18.-22. September 2023. In: Machine Learning and Knowledge Discovery in Databases: Research Track, Lecture Notes in Computer Science Bd. 14169 Cham: Springer. S. 459-474

Abstract

Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approaches asymptotically recover the true posterior but are considered prohibitively expensive for large modern architectures. Local methods, which have emerged as a popular alternative, focus on specific parameter regions that can be approximated by functions with tractable integrals. While these often yield satisfactory empirical results, they fail, by definition, to account for the multi-modality of the parameter posterior. Such coarse approximations can be detrimental in practical applications, notably safety-critical ones. In this work, we argue that the dilemma between exact-but-unaffordable and cheap-but-inexact approaches can be mitigated by exploiting symmetries in the posterior landscape. These symmetries, induced by neuron interchangeability and certain activation functions, manifest in different parameter values leading to the same functional output value. We show theoretically that the posterior predictive density in Bayesian neural networks can be restricted to a symmetry-free parameter reference set. By further deriving an upper bound on the number of Monte Carlo chains required to capture the functional diversity, we propose a straightforward approach for feasible Bayesian inference. Our experiments suggest that efficient sampling is indeed possible, opening up a promising path to accurate uncertainty quantification in deep learning.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Statistik ; Mathematik, Informatik und Statistik > Statistik > Lehrstühle/Arbeitsgruppen > Computationale Statistik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik ; 500 Naturwissenschaften und Mathematik > 510 Mathematik
• ISBN: 978-3-031-43411-2 ; 978-3-031-43412-9
• ISSN: 0302-9743
• Ort: Cham
• Sprache: Englisch