Web: http://arxiv.org/abs/2206.07438

June 16, 2022, 1:12 a.m. | Florian Karl, Tobias Pielok, Julia Moosbauer, Florian Pfisterer, Stefan Coors, Martin Binder, Lennart Schneider, Janek Thomas, Jakob Richter, Michel L

stat.ML updates on arXiv.org arxiv.org

Hyperparameter optimization constitutes a large part of typical modern
machine learning workflows. This arises from the fact that machine learning
methods and corresponding preprocessing steps often only yield optimal
performance when hyperparameters are properly tuned. But in many applications,
we are not only interested in optimizing ML pipelines solely for predictive
accuracy; additional metrics or constraints must be considered when determining
an optimal configuration, resulting in a multi-objective optimization problem.
This is often neglected in practice, due to a lack …

arxiv lg optimization overview

More from arxiv.org / stat.ML updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY