Incorporating Interpretable Output Constraints in Bayesian Neural Networks
Abstract
Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness. We introduce a novel probabilistic framework for reasoning with such constraints and formulate a prior that enables us to effectively incorporate them into Bayesian neural networks (BNNs), including a variant that can be amortized over tasks. The resulting Output-Constrained BNN (OC-BNN) is fully consistent with the Bayesian framework for uncertainty quantification and is amenable to black-box inference. Unlike typical BNN inference in uninterpretable parameter space, OC-BNNs widen the range of functional knowledge that can be incorporated, especially for model users without expertise in machine learning. We demonstrate the efficacy of OC-BNNs on real-world datasets, spanning multiple domains such as healthcare, criminal justice, and credit scoring.
Cite
@article{arxiv.2010.10969,
title = {Incorporating Interpretable Output Constraints in Bayesian Neural Networks},
author = {Wanqian Yang and Lars Lorch and Moritz A. Graule and Himabindu Lakkaraju and Finale Doshi-Velez},
journal= {arXiv preprint arXiv:2010.10969},
year = {2021}
}
Comments
11 pages, with six supplementary pages. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. Code available at: https://github.com/dtak/ocbnn-public. Updated version (final, official submission to NeurIPS in January 2021) includes post-conference revisions: improved results in Section 6.2, and corrected minor errata in Appendix C