Bayesian coresets: Revisiting the nonconvex optimization perspective
Zhang, Yibo
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https://hdl.handle.net/2142/115776
Description
Title
Bayesian coresets: Revisiting the nonconvex optimization perspective
Author(s)
Zhang, Yibo
Issue Date
2022-04-26
Director of Research (if dissertation) or Advisor (if thesis)
Koyejo, Oluwasanmi
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Bayesian Coreset
Optimization
Non-Convex
IHT
Iterative Hard Thresholding
Abstract
Bayesian coresets have emerged as a promising approach for implementing scalable Bayesian inference. The Bayesian coreset problem involves selecting a (weighted) subset of the data samples, such that the posterior inference using the selected subset closely approximates the posterior inference using the full dataset. This manuscript revisits Bayesian coresets through the lens of sparsity constrained optimization. Leveraging recent advances in accelerated optimization methods, we propose and analyze a novel algorithm for coreset selection. We provide explicit convergence rate guarantees and present an empirical evaluation on a variety of benchmark datasets to highlight our proposed algorithm’s superior performance compared to state-of-the-art on speed and accuracy.
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