Cited by 303Advanced Bandit Algorithms ResearchAdvanced Multi-Objective Optimization AlgorithmsMachine Learning and Data Classification

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scikit-optimize/scikit-optimize

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Head, Tim, Kumar, Manoj, Nahrstaedt, Holger, Louppe, Gilles, Shcherbatyi, Iaroslav·2021·Zenodo

DOI 10.5281/zenodo.1157319

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Abstract

Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization. skopt aims to be accessible and easy to use in many contexts.

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Authors: Head, T., Kumar, M., Nahrstaedt, H., Louppe, G., & Shcherbatyi, I.
Affiliations: Wild Tree Tech
Google Brain
University of Liège (BE)
Saarland University (DE)
ORCID: Head, Tim
Nahrstaedt, Holger
Louppe, Gilles
Shcherbatyi, Iaroslav
Venue: Zenodo
Publisher: Zenodo
Published: 2021-10-12
DOI: 10.5281/zenodo.1157319
OpenAlex: W6930339331
Cited by: 302
References: 0
Citations by year: 2026: 23 · 2025: 42 · 2024: 47 · 2023: 48 · 2022: 46 · 2021: 60
Type: software
Language: en
Open access: green
zenodo.org
Licence: other-oa
opensource.org/licenses/BSD-3-Clause
Locations: Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
Zenodo (CERN European Organization for Nuclear Research)
Zenodo (CERN European Organization for Nuclear Research) (other-oa)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Keywords: black-box optimization, sequential model-based optimization, hyperparameter optimization, surrogate models

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