A proximal Newton adaptive importance sampler
File(s)
Accepted version
OA Location
Author(s)
Elvira, Víctor
Chouzenoux, Émilie
Akyildiz, O Deniz
Type
Journal Article
Abstract
Adaptive importance sampling (AIS) algorithms are a rising methodology in signal processing, statistics, and machine learning. An effective adaptation of the proposals is key for the success of AIS. Recent works have shown that gradient information about the involved target density can greatly boost performance, but its applicability is restricted to differentiable targets. In this letter, we propose a proximal Newton adaptive importance sampler for the estimation of expectations with respect to non-smooth target distributions. We implement a scaled Newton proximal gradient method to adapt the proposal distributions, enabling efficient and optimized moves even when the target distribution lacks differentiability. We show the good performance of the algorithm in two scenarios: one with convex constraints and another with non-smooth sparse priors.
Date Issued
2025-01-01
Date Acceptance
2025-03-18
Citation
IEEE Signal Processing Letters, 2025, 32, pp.1545-1549
ISSN
1070-9908
Publisher
Institute of Electrical and Electronics Engineers
Start Page
1545
End Page
1549
Journal / Book Title
IEEE Signal Processing Letters
Volume
32
Copyright Statement
Copyright © 2025 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
Publication Status
Published
Date Publish Online
2025-03-21