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Gradient-Free Kernel Stein Discrepancy

Lookup NU author(s): Matthew FisherORCiD, Professor Chris OatesORCiD

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Abstract

© 2023 Neural information processing systems foundation. All rights reserved.Stein discrepancies have emerged as a powerful statistical tool, being applied to fundamental statistical problems including parameter inference, goodness-of-fit testing, and sampling. The canonical Stein discrepancies require the derivatives of a statistical model to be computed, and in return provide theoretical guarantees of convergence detection and control. However, for complex statistical models, the stable numerical computation of derivatives can require bespoke algorithmic development and render Stein discrepancies impractical. This paper focuses on posterior approximation using Stein discrepancies, and introduces a collection of non-canonical Stein discrepancies that are gradient-free, meaning that derivatives of the statistical model are not required. Sufficient conditions for convergence detection and control are established, and applications to sampling and variational inference are presented.

Publication metadata

Author(s): Fisher MA, Oates CJ

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Advances in Neural Information Processing Systems 37 (NeurIPS 2023)

Year of Conference: 2023

Acceptance date: 02/04/2023

Publisher: Neural Information Processing Systems Foundation

URL: https://proceedings.neurips.cc/paper_files/paper/2023/hash/4b4d25dc0c52d3cf43d5b203cdfdf241-Abstract-Conference.html

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