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TS-RSR: A Provably Efficient Approach for Batch Bayesian Optimization

SIAM Journal on Optimization(2025)10.1137/24m1675102Source: DataRank Database

TS-RSR: A Provably Efficient Approach for Batch Bayesian Optimization is a research paper published in SIAM Journal on Optimization (2025). On theSindex it has a DataRank of 0.

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (6 grants)Topics (10)

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Authors (2)

Na Li ORCID,Zhaolin Ren ORCID

Live enrichment

National Institutes of Health

Grant: R01LM014465

National Science Foundation

Grant: 2112085

AI Institute in Dynamic Systems

National Science Foundation

Grant: ONR N000142512173

National Science Foundation

Grant: 2038603

CPS: Medium: An AI-enabled Cyber-Physical-Biological System for Cardiac Organoid Maturation

National Institutes of Health

Grant: 1R01LM014465-01

SCH: Al-driven Flexible Electronics for Cardiac Organoid Maturation

National Science Foundation

Grant: 2328241

ASCENT: From sensors to multiscale digital twin to autonomous operation of resilient electric power grids

Fields of Study

0101 mathematics01 natural sciences

Keywords

FOS: Computer and information sciencesComputer Science - Machine LearningStatistics - Machine LearningOptimization and Control (math.OC)FOS: MathematicsMachine Learning (stat.ML)Mathematics - Optimization and ControlMachine Learning (cs.LG)