Feb. 12, 2024, 5:43 a.m. | Quang-Huy Nguyen Long P. Hoang Hoang V. Viet Dung D. Le

cs.LG updates on arXiv.org arxiv.org

Pareto Set Learning (PSL) is a promising approach for approximating the entire Pareto front in multi-objective optimization (MOO) problems. However, existing derivative-free PSL methods are often unstable and inefficient, especially for expensive black-box MOO problems where objective function evaluations are costly. In this work, we propose to address the instability and inefficiency of existing PSL methods with a novel controllable PSL method, called Co-PSL. Particularly, Co-PSL consists of two stages: (1) warm-starting Bayesian optimization to obtain quality Gaussian Processes priors …

bayesian box cs.lg free function math.oc multi-objective optimization pareto set warm work

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Encounter Data Management Professional

@ Humana | Work at Home - Kentucky

Pre-sales Manager (Data, Analytics & AI)

@ Databricks | Stockholm, Sweden

Lecturer / Senior Lecturer - Medical Imaging

@ Central Queensland University | Mackay, QLD, AU

Intern - Research Engineer

@ Plus | Santa Clara, CA