all AI news
Expected hypervolume improvement for simultaneous multi-objective and multi-fidelity optimization. (arXiv:2112.13901v2 [cs.LG] UPDATED)
Jan. 3, 2022, 2:10 a.m. | Faran Irshad, Stefan Karsch, Andreas Döpp
cs.LG updates on arXiv.org arxiv.org
Bayesian optimization has proven to be an efficient method to optimize
expensive-to-evaluate systems. However, depending on the cost of single
observations, multi-dimensional optimizations of one or more objectives may
still be prohibitively expensive. Multi-fidelity optimization remedies this
issue by including multiple, cheaper information sources such as low-resolution
approximations in numerical simulations. Acquisition functions for
multi-fidelity optimization are typically based on exploration-heavy algorithms
that are difficult to combine with optimization towards multiple objectives.
Here we show that the expected hypervolume improvement …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Data Architect
@ Western Digital | San Jose, CA, United States
Senior Data Scientist GenAI (m/w/d)
@ Deutsche Telekom | Bonn, Deutschland
Senior Data Engineer, Telco (Remote)
@ Lightci | Toronto, Ontario
Consultant Data Architect/Engineer H/F - Innovative Tech
@ Devoteam | Lyon, France
(Senior) ML Engineer / Software Engineer Machine Learning & AI (m/f/x) onsite or remote (in Germany or Austria)
@ Scalable GmbH | Wien, Germany