May 12, 2022, 1:11 a.m. | Hao Wang, Kaifeng Yang, Michael Affenzeller, Michael Emmerich

cs.LG updates on arXiv.org arxiv.org

This work provides the exact expression of the probability distribution of
the hypervolume improvement (HVI) for bi-objective generalization of Bayesian
optimization. Here, instead of a single-objective improvement, we consider the
improvement of the hypervolume indicator concerning the current best
approximation of the Pareto front. Gaussian process regression models are
trained independently on both objective functions, resulting in a bi-variate
separated Gaussian distribution serving as a predictive model for the
vector-valued objective function. Some commonly HVI-based acquisition functions
(probability of improvement …

arxiv bayesian bi distribution improvement optimization probability

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Business Intelligence Analyst

@ Rappi | COL-Bogotá

Applied Scientist II

@ Microsoft | Redmond, Washington, United States