Limbo

Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization - Published in JOSS (2018)

https://github.com/resibots/limbo

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 6 DOI reference(s) in README and JOSS metadata
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Academic publication links
Links to: arxiv.org, nature.com, joss.theoj.org
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Committers with academic emails
4 of 15 committers (26.7%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
Published in Journal of Open Source Software

Keywords

bayesian-optimization gaussian-processes optimization-framework optimization-library

Keywords from Contributors

dart eigen3 magnum

Last synced: 4 months ago · JSON representation

Repository

A lightweight framework for Gaussian processes and Bayesian optimization of black-box functions (C++11)

Basic Info

Host: GitHub
Owner: resibots
License: other
Language: C++
Default Branch: master
Homepage: http://www.resibots.eu/limbo
Size: 85.6 MB

Statistics

Stars: 255
Watchers: 32
Forks: 58
Open Issues: 20
Releases: 3

Topics

bayesian-optimization gaussian-processes optimization-framework optimization-library

Created over 11 years ago · Last pushed about 2 years ago

Metadata Files

Readme Contributing License

limbo

Limbo (LIbrary for Model-Based Optimization) is an open-source C++11 library for Gaussian Processes and data-efficient optimization (e.g., Bayesian optimization) that is designed to be both highly flexible and very fast. It can be used as a state-of-the-art optimization library or to experiment with novel algorithms with "plugin" components.

Documentation & Versions

The development branch is the master branch. For the latest stable release, check the release-2.1 branch. Documentation is available at: http://www.resibots.eu/limbo

Citing Limbo

If you use Limbo in a scientific paper, please cite:

Cully, A., Chatzilygeroudis, K., Allocati, F., and Mouret J.-B., (2018). Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization. The Journal of Open Source Software.

In BibTex:

@article{cully2018limbo,
    title={{Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization}},
    author={Cully, A. and Chatzilygeroudis, K. and Allocati, F.  and Mouret, J.-B.},
    year={2018},
    journal={{The Journal of Open Source Software}},
    publisher={The Open Journal},
    volume={3},
    number={26},
    pages={545},
    doi={10.21105/joss.00545}
}

Authors

Antoine Cully (Imperial College): http://www.antoinecully.com
Jean-Baptiste Mouret (Inria): http://members.loria.fr/JBMouret
Konstantinos Chatzilygeroudis (Inria): http://costashatz.github.io/
Federico Allocati (Inria)

Other contributors

Vaios Papaspyros (Inria)
Roberto Rama (Inria)

Limbo is partly funded by the ResiBots ERC Project (http://www.resibots.eu).

Main features

Implementation of the classic algorithms (Bayesian optimization, many kernels, likelihood maximization, etc.)
Modern C++-11
Generic framework (template-based / policy-based design), which allows for easy customization, to test novel ideas
Experimental framework that allows user to easily test variants of experiments, compare treatments, submit jobs to clusters (OAR scheduler), etc.
High performance (in particular, Limbo can exploit multi-core computers via Intel TBB and vectorize some operations via Eigen3)
Purposely small to be easily maintained and quickly understood

Scientific articles that use Limbo

Chatzilygeroudis, K., & Mouret, J. B. (2018). Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics. Proceedings of the International Conference on Robotics and Automation (ICRA).
Pautrat, R., Chatzilygeroudis, K., & Mouret, J.-B. (2018). Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search. Proceedings of the International Conference on Robotics and Automation (ICRA).
Chatzilygeroudis, K., Vassiliades, V. and Mouret, J.-B. (2017). Reset-free Trial-and-Error Learning for Robot Damage Recovery. Robotics and Autonomous Systems.
Karban P., Pánek D., Mach F. and Doležel, I. (2017). Calibration of numerical models based on advanced optimization and penalization techniques. Journal of Electrical Engineering, 68(5), 396-400.
Chatzilygeroudis K., Rama R., Kaushik, R., Goepp, D., Vassiliades, V. and Mouret, J.-B. (2017). Black-Box Data-efficient Policy Search for Robotics. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Tarapore, D., Clune, J., Cully, A., and Mouret, J.-B. (2016). How Do Different Encodings Influence the Performance of the MAP-Elites Algorithm?. In Proc. of Genetic and Evolutionary Computation Conference.
Cully, A., Clune, J., Tarapore, D., and Mouret, J.-B. (2015). Robots that can adapt like animals. Nature, 521(7553), 503-507.
Chatzilygeroudis, K., Cully, A. and Mouret, J.-B. (2016). Towards semi-episodic learning for robot damage recovery. Workshop on AI for Long-Term Autonomy at the IEEE International Conference on Robotics and Automation 2016.
Papaspyros, V., Chatzilygeroudis, K., Vassiliades, V., and Mouret, J.-B. (2016). Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors. Workshop on Bayesian Optimization at the Annual Conference on Neural Information Processing Systems (NIPS) 2016.

Research projects that use Limbo

Resibots. ERC Starting Grant: http://www.resibots.eu/
PAL. H2020 EU project: http://www.pal4u.eu/

Owner

Name: ResiBots
Login: resibots
Kind: organization
Email: jean-baptiste.mouret@inria.fr
Location: Inria, France

Website: https://www.resibots.eu/git_guide.html
Repositories: 81
Profile: https://github.com/resibots

ResiBots ERC project - Guide: https://www.resibots.eu/git_guide.html

JOSS Publication

Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization

Published

June 26, 2018

DOI

10.21105/joss.00545

Volume 3, Issue 26, Page 545

Authors

Antoine Cully

Personal Robotics Lab, Imperial College London, London, United Kingdom

Konstantinos Chatzilygeroudis

Inria, CNRS, Université de Lorraine, LORIA, Nancy, France

Federico Allocati
Inria, CNRS, Université de Lorraine, LORIA, Nancy, France

Jean-Baptiste Mouret

Inria, CNRS, Université de Lorraine, LORIA, Nancy, France

Editor

Arfon Smith

GitHub Events

Total

Issues event: 1
Watch event: 12
Issue comment event: 3
Pull request event: 2
Fork event: 3

Last Year

Issues event: 1
Watch event: 12
Issue comment event: 3
Pull request event: 2
Fork event: 3

Committers

Last synced: 5 months ago

All Time

Total Commits: 1,248
Total Committers: 15
Avg Commits per committer: 83.2
Development Distribution Score (DDS): 0.583

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Konstantinos Chatzilygeroudis	c**z@g**m	521
Jean-Baptiste Mouret	m**t@i**r	342
Federico Allocati	f**i@g**m	210
Dorian Goepp	d**p@i**r	60
Antoine Cully	a**y@i**k	46
Roberto Rama	r**a@i**r	24
Vaios Papaspyros	b**s@g**m	19
Aneoshun	c**y@i**r	11
Matias Mattamala	m**s@r**k	5
Christoph Neuhauser	c**r@g**m	3
Kazu Otani	k**4@g**m	2
cully	c**y@c**r	2
Arfon Smith	a****n	1
Baptiste Busch	b**h@g**m	1
Cully	t**o@t**)	1

Committer Domains (Top 20 + Academic)

inria.fr: 2 isir.upmc.fr: 2 tedesco-system-product-name.(none): 1 robots.ox.ac.uk: 1 imperial.ac.uk: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 62
Total pull requests: 45
Average time to close issues: 7 months
Average time to close pull requests: 19 days
Total issue authors: 24
Total pull request authors: 9
Average comments per issue: 3.73
Average comments per pull request: 2.4
Merged pull requests: 39
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 1
Pull requests: 1
Average time to close issues: N/A
Average time to close pull requests: 3 minutes
Issue authors: 1
Pull request authors: 1
Average comments per issue: 5.0
Average comments per pull request: 0.0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

langongjin (14)
costashatz (10)
jbmouret (7)
kirilllzaitsev (4)
123epsilon (4)
nickanthony-dgl (3)
jussihi (2)
ghost (2)
riturajkaushik (1)
pan3rock (1)
LorisR (1)
AranBSmith (1)
MUXIANGRU (1)
LucaCerina (1)
Giuseppe5 (1)

Pull Request Authors

costashatz (22)
jbmouret (14)
kazuotani14 (2)
ryadgar (2)
chrismile (2)
mmattamala (1)
bpapaspyros (1)
arfon (1)
nickanthony-dgl (1)

Top Labels

Issue Labels

enhancement (7) bug (3) wontfix (1)

Pull Request Labels

bug (1) enhancement (1)

Packages

Total packages: 1
Total downloads: unknown

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 3

proxy.golang.org: github.com/resibots/limbo

Documentation: https://pkg.go.dev/github.com/resibots/limbo#section-documentation
License: other
Latest release: v2.1.0+incompatible
published over 7 years ago

Versions: 3
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 4 months ago

Limbo

Science Score: 95.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

limbo

Documentation & Versions

Citing Limbo

Authors

Other contributors

Main features

Scientific articles that use Limbo

Research projects that use Limbo

Owner

JOSS Publication

Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization

Authors

Editor

Tags

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

proxy.golang.org: github.com/resibots/limbo

Rankings