Work

Bayesian Optimization with Exponential Convergence

Year: 2016

Type: preprint

Abstract: This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the delta-cover sampling. Most Bayesian optimization methods require auxiliary optimization: an additional non-convex global optimization problem, which can be time-consuming and hard to implement in practice. Also, the existing Bayesian optimization method with exponential convergence requires access to the delta-cover sampling, which was considered to be impractical. Our approach eliminates both requirements and achieves an exponential convergence rate.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1604.01348

Authors: Kawaguchi, Kenji, Kaelbling, Leslie Pack, Lozano-Pérez, Tomás

Institutions: IIT@MIT, Massachusetts Institute of Technology

Language: English

Topic: Advanced Bandit Algorithms Research

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funders: National Science Foundation, Office of Naval Research, Army Research Office

Awards: G3802117657, G8510421867, G8876996369

OpenAlex ID: W2187386544

Cites: 22

Cited by: 80

Related to: 20