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

The DOI matches its DataCite record, with a small difference in title or year.

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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Metadata

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
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
Licence
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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