June 10, 2024, 4:44 a.m. | Ji Won Park, Nata\v{s}a Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho

stat.ML updates on arXiv.org arxiv.org

arXiv:2306.00344v2 Announce Type: replace-cross
Abstract: Many scientific and industrial applications require the joint optimization of multiple, potentially competing objectives. Multi-objective Bayesian optimization (MOBO) is a sample-efficient framework for identifying Pareto-optimal solutions. At the heart of MOBO is the acquisition function, which determines the next candidate to evaluate by navigating the best compromises among the objectives. In this paper, we show a natural connection between non-dominated solutions and the extreme quantile of the joint cumulative distribution function (CDF). Motivated by this …

abstract acquisition applications arxiv bayesian cs.lg framework function heart industrial multi-objective multiple multivariate next optimization pareto replace sample scientific solutions stat.ml type

Senior Data Engineer

@ Displate | Warsaw

Solution Architect

@ Philips | Bothell - B2 - Bothell 22050

Senior Product Development Engineer - Datacenter Products

@ NVIDIA | US, CA, Santa Clara

Systems Engineer - 2nd Shift (Onsite)

@ RTX | PW715: Asheville Site W Asheville Greenfield Site TBD , Asheville, NC, 28803 USA

System Test Engineers (HW & SW)

@ Novanta | Barcelona, Spain

Senior Solutions Architect, Energy

@ NVIDIA | US, TX, Remote