2026-09-21T05:39:55Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/115776 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Koyejo, Oluwasanmi 2022-05 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms The student, Yibo Zhang, accepted the attached license on 2022-04-24 at 17:49. The student, Yibo Zhang, submitted this Thesis for approval on 2022-04-24 at 17:55. This Thesis was approved for publication on 2022-04-26 at 11:37. DSpace SAF Submission Ingestion Package generated from Vireo submission #17942 on 2022-11-11 at 17:38:04 Bayesian coresets: Revisiting the nonconvex optimization perspective Zhang, Yibo 2022-04-26 Bayesian Coreset Optimization Non-Convex IHT Iterative Hard Thresholding 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. Thesis eng https://hdl.handle.net/2142/115776 Copyright 2022 Yibo Zhang M.S. Thesis Computer Science University of Illinois at Urbana-Champaign Computer Science