Invariance-Aware Randomized Smoothing Certificates

Jan Schuchardt, Stephan Günnemann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

3 Zitate (Scopus)

Abstract

Building models that comply with the invariances inherent to different domains, such as invariance under translation or rotation, is a key aspect of applying machine learning to real world problems like molecular property prediction, medical imaging, protein folding or LiDAR classification. For the first time, we study how the invariances of a model can be leveraged to provably guarantee the robustness of its predictions. We propose a gray-box approach, enhancing the powerful black-box randomized smoothing technique with white-box knowledge about invariances. First, we develop gray-box certificates based on group orbits, which can be applied to arbitrary models with invariance under permutation and Euclidean isometries. Then, we derive provably tight gray-box certificates. We experimentally demonstrate that the provably tight certificates can offer much stronger guarantees, but that in practical scenarios the orbit-based method is a good approximation.

OriginalspracheEnglisch
TitelAdvances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022
Redakteure/-innenS. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh
Herausgeber (Verlag)Neural information processing systems foundation
ISBN (elektronisch)9781713871088
PublikationsstatusVeröffentlicht - 2022
Veranstaltung36th Conference on Neural Information Processing Systems, NeurIPS 2022 - New Orleans, USA/Vereinigte Staaten
Dauer: 28 Nov. 20229 Dez. 2022

Publikationsreihe

NameAdvances in Neural Information Processing Systems
Band35
ISSN (Print)1049-5258

Konferenz

Konferenz36th Conference on Neural Information Processing Systems, NeurIPS 2022
Land/GebietUSA/Vereinigte Staaten
OrtNew Orleans
Zeitraum28/11/229/12/22

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