Bayesian optimization (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Bayesian optimization" in English language version.

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9arxiv.org

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2springer.com

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2semanticscholar.org

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1bayesoptbook.com

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1probabilistic-numerics.org

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1neurips.cc

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1github.com

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1warwick.ac.uk

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aaai.org

Daniel J. Lizotte, Tao Wang, Michael H. Bowling, Dale Schuurmans: Automatic Gait Optimization with Gaussian Process Regression Archived 2017-08-12 at the Wayback Machine. International Joint Conference on Artificial Intelligence: 944–949 (2007)

acm.org

dl.acm.org

Kent, Paul; Branke, Juergen (2023-07-12). "Bayesian Quality Diversity Search with Interactive Illumination". Proceedings of the Genetic and Evolutionary Computation Conference (PDF). GECCO '23. New York, NY, USA: Association for Computing Machinery. pp. 1019–1026. doi:10.1145/3583131.3590486. ISBN 979-8-4007-0119-1. S2CID 259833672.

acs.org

pubs.acs.org

Gomez-Bombarelli et al. Automatic Chemical Design using a Data-Driven Continuous Representation of Molecules. ACS Central Science, Volume 4, Issue 2, 268-276 (2018)

archive.org

scholar.archive.org

Garnett, Roman; Osborne, Michael A.; Roberts, Stephen J. (2010). "Bayesian optimization for sensor set selection". In Abdelzaher, Tarek F.; Voigt, Thiemo; Wolisz, Adam (eds.). Proceedings of the 9th International Conference on Information Processing in Sensor Networks, IPSN 2010, April 12–16, 2010, Stockholm, Sweden. ACM. pp. 209–219. doi:10.1145/1791212.1791238. ISBN 978-1-60558-988-6.

arxiv.org

Snoek, Jasper (2012). "Practical Bayesian Optimization of Machine Learning Algorithms". Advances in Neural Information Processing Systems 25 (NIPS 2012). 25. arXiv:1206.2944.
Klein, Aaron (2017). "Fast bayesian optimization of machine learning hyperparameters on large datasets". Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR: 528–536. arXiv:1605.07079.
Frazier, Peter I. (2018-07-08). "A Tutorial on Bayesian Optimization". arXiv:1807.02811 [stat.ML].
Eric Brochu, Vlad M. Cora, Nando de Freitas: A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. CoRR abs/1012.2599 (2010)
Yuki Koyama, Issei Sato, Masataka Goto: Sequential Gallery for Interactive Visual Design Optimization. ACM Transactions on Graphics, Volume 39, Issue 4, pp.88:1–88:12 (2020). DOI: https://doi.org/10.1145/3386569.3392444
Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. KDD 2013: 847–855
Philip Ilten, Mike Williams, Yunjie Yang. Event generator tuning using Bayesian optimization. 2017 JINST 12 P04028. DOI: 10.1088/1748-0221/12/04/P04028
Kent, Paul; Gaier, Adam; Mouret, Jean-Baptiste; Branke, Juergen (2023-07-19). "BOP-Elites, a Bayesian Optimisation Approach to Quality Diversity Search with Black-Box descriptor functions". arXiv:2307.09326 [math.OC]. Preprint: Arxiv.
Gaier, Adam; Asteroth, Alexander; Mouret, Jean-Baptiste (2018-09-01). "Data-Efficient Design Exploration through Surrogate-Assisted Illumination". Evolutionary Computation. 26 (3): 381–410. arXiv:1806.05865. doi:10.1162/evco_a_00231. ISSN 1063-6560. PMID 29883202. S2CID 47003986.

bayesoptbook.com

Garnett, Roman (2023). Bayesian Optimization. Cambridge University Press. ISBN 978-1-108-42578-0.

doi.org

Močkus, Jonas (1975). "On bayesian methods for seeking the extremum". Optimization Techniques IFIP Technical Conference Novosibirsk, July 1–7, 1974. Lecture Notes in Computer Science. Vol. 27. pp. 400–404. doi:10.1007/3-540-07165-2_55. ISBN 978-3-540-07165-5.
Yuki Koyama, Issei Sato, Daisuke Sakamoto, Takeo Igarashi: Sequential Line Search for Efficient Visual Design Optimization by Crowds. ACM Transactions on Graphics, Volume 36, Issue 4, pp.48:1–48:11 (2017). DOI: https://doi.org/10.1145/3072959.3073598
Yuki Koyama, Issei Sato, Masataka Goto: Sequential Gallery for Interactive Visual Design Optimization. ACM Transactions on Graphics, Volume 39, Issue 4, pp.88:1–88:12 (2020). DOI: https://doi.org/10.1145/3386569.3392444
Garnett, Roman; Osborne, Michael A.; Roberts, Stephen J. (2010). "Bayesian optimization for sensor set selection". In Abdelzaher, Tarek F.; Voigt, Thiemo; Wolisz, Adam (eds.). Proceedings of the 9th International Conference on Information Processing in Sensor Networks, IPSN 2010, April 12–16, 2010, Stockholm, Sweden. ACM. pp. 209–219. doi:10.1145/1791212.1791238. ISBN 978-1-60558-988-6.
Berkenkamp, Felix (2019). Safe Exploration in Reinforcement Learning: Theory and Applications in Robotics (Doctoral Thesis thesis). ETH Zurich. doi:10.3929/ethz-b-000370833. hdl:20.500.11850/370833.
Kent, Paul; Branke, Juergen (2023-07-12). "Bayesian Quality Diversity Search with Interactive Illumination". Proceedings of the Genetic and Evolutionary Computation Conference (PDF). GECCO '23. New York, NY, USA: Association for Computing Machinery. pp. 1019–1026. doi:10.1145/3583131.3590486. ISBN 979-8-4007-0119-1. S2CID 259833672.
Gaier, Adam; Asteroth, Alexander; Mouret, Jean-Baptiste (2018-09-01). "Data-Efficient Design Exploration through Surrogate-Assisted Illumination". Evolutionary Computation. 26 (3): 381–410. arXiv:1806.05865. doi:10.1162/evco_a_00231. ISSN 1063-6560. PMID 29883202. S2CID 47003986.

epfl.ch

infoscience.epfl.ch

Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias W. Seeger: Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting. IEEE Transactions on Information Theory 58(5):3250–3265 (2012)

ethz.ch

research-collection.ethz.ch

Berkenkamp, Felix (2019). Safe Exploration in Reinforcement Learning: Theory and Applications in Robotics (Doctoral Thesis thesis). ETH Zurich. doi:10.3929/ethz-b-000370833. hdl:20.500.11850/370833.

github.com

Wilson, Samuel (2019-11-22), ParBayesianOptimization R package, retrieved 2019-12-12

haikufactory.com

Eric Brochu, Tyson Brochu, Nando de Freitas: A Bayesian Interactive Optimization Approach to Procedural Animation Design. Symposium on Computer Animation 2010: 103–112

handle.net

hdl.handle.net

Berkenkamp, Felix (2019). Safe Exploration in Reinforcement Learning: Theory and Applications in Robotics (Doctoral Thesis thesis). ETH Zurich. doi:10.3929/ethz-b-000370833. hdl:20.500.11850/370833.

iop.org

iopscience.iop.org

Evaristo Cisbani et al. AI-optimized detector design for the future Electron-Ion Collider: the dual-radiator RICH case 2020 JINST 15 P05009. DOI: 10.1088/1748-0221/15/05/P05009

koyama.xyz

Yuki Koyama, Issei Sato, Daisuke Sakamoto, Takeo Igarashi: Sequential Line Search for Efficient Visual Design Optimization by Crowds. ACM Transactions on Graphics, Volume 36, Issue 4, pp.48:1–48:11 (2017). DOI: https://doi.org/10.1145/3072959.3073598

mlr.press

proceedings.mlr.press

Klein, Aaron (2017). "Fast bayesian optimization of machine learning hyperparameters on large datasets". Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR: 528–536. arXiv:1605.07079.

neurips.cc

proceedings.neurips.cc

Snoek, Jasper (2012). "Practical Bayesian Optimization of Machine Learning Algorithms". Advances in Neural Information Processing Systems 25 (NIPS 2012). 25. arXiv:1206.2944.

nih.gov

pubmed.ncbi.nlm.nih.gov

Gaier, Adam; Asteroth, Alexander; Mouret, Jean-Baptiste (2018-09-01). "Data-Efficient Design Exploration through Surrogate-Assisted Illumination". Evolutionary Computation. 26 (3): 381–410. arXiv:1806.05865. doi:10.1162/evco_a_00231. ISSN 1063-6560. PMID 29883202. S2CID 47003986.

nips.cc

papers.nips.cc

J. S. Bergstra, R. Bardenet, Y. Bengio, B. Kégl: Algorithms for Hyper-Parameter Optimization. Advances in Neural Information Processing Systems: 2546–2554 (2011)
Eric Brochu, Nando de Freitas, Abhijeet Ghosh: Active Preference Learning with Discrete Choice Data. Advances in Neural Information Processing Systems: 409-416 (2007)
J. Snoek, H. Larochelle, R. P. Adams Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems: 2951-2959 (2012)
Jasper Snoek, Hugo Larochelle and Ryan Prescott Adams. Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems, 2012

probabilistic-numerics.org

Hennig, P.; Osborne, M. A.; Kersting, H. P. (2022). Probabilistic Numerics (PDF). Cambridge University Press. pp. 243–278. ISBN 978-1107163447.

psu.edu

citeseerx.ist.psu.edu

J. Bergstra, D. Yamins, D. D. Cox (2013). Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms. Proc. SciPy 2013.

rsc.org

pubs.rsc.org

Griffiths et al. Constrained Bayesian Optimization for Automatic Chemical Design using Variational Autoencoders Chemical Science: 11, 577-586 (2020)

sagepub.com

ijr.sagepub.com

Scott Kuindersma, Roderic Grupen, and Andrew Barto. Variable Risk Control via Stochastic Optimization. International Journal of Robotics Research, volume 32, number 7, pp 806–825 (2013)

semanticscholar.org

api.semanticscholar.org

Kent, Paul; Branke, Juergen (2023-07-12). "Bayesian Quality Diversity Search with Interactive Illumination". Proceedings of the Genetic and Evolutionary Computation Conference (PDF). GECCO '23. New York, NY, USA: Association for Computing Machinery. pp. 1019–1026. doi:10.1145/3583131.3590486. ISBN 979-8-4007-0119-1. S2CID 259833672.
Gaier, Adam; Asteroth, Alexander; Mouret, Jean-Baptiste (2018-09-01). "Data-Efficient Design Exploration through Surrogate-Assisted Illumination". Evolutionary Computation. 26 (3): 381–410. arXiv:1806.05865. doi:10.1162/evco_a_00231. ISSN 1063-6560. PMID 29883202. S2CID 47003986.

springer.com

link.springer.com

Ruben Martinez-Cantin, Nando de Freitas, Eric Brochu, Jose Castellanos and Arnaud Doucet. A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot. Autonomous Robots. Volume 27, Issue 2, pp 93–103 (2009)
Roberto Calandra, André Seyfarth, Jan Peters, and Marc P. Deisenroth Bayesian optimization for learning gaits under uncertainty. Ann. Math. Artif. Intell. Volume 76, Issue 1, pp 5-23 (2016) DOI:10.1007/s10472-015-9463-9

ubc.ca

cs.ubc.ca

Frank Hutter, Holger Hoos, and Kevin Leyton-Brown (2011). Sequential model-based optimization for general algorithm configuration, Learning and Intelligent Optimization

warwick.ac.uk

wrap.warwick.ac.uk

Kent, Paul; Branke, Juergen (2023-07-12). "Bayesian Quality Diversity Search with Interactive Illumination". Proceedings of the Genetic and Evolutionary Computation Conference (PDF). GECCO '23. New York, NY, USA: Association for Computing Machinery. pp. 1019–1026. doi:10.1145/3583131.3590486. ISBN 979-8-4007-0119-1. S2CID 259833672.

web.archive.org

Daniel J. Lizotte, Tao Wang, Michael H. Bowling, Dale Schuurmans: Automatic Gait Optimization with Gaussian Process Regression Archived 2017-08-12 at the Wayback Machine. International Joint Conference on Artificial Intelligence: 944–949 (2007)

worldcat.org

search.worldcat.org

Gaier, Adam; Asteroth, Alexander; Mouret, Jean-Baptiste (2018-09-01). "Data-Efficient Design Exploration through Surrogate-Assisted Illumination". Evolutionary Computation. 26 (3): 381–410. arXiv:1806.05865. doi:10.1162/evco_a_00231. ISSN 1063-6560. PMID 29883202. S2CID 47003986.