Bayesian optimization: Difference between revisions

Revision as of 20:36, 23 August 2023

Revision as of 22:37, 23 August 2023

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==History==

The term is generally attributed to {{ill|Jonas Mockus|lt}} and is coined in his work from a series of publications on global optimization in the 1970s and 1980s.<ref>{{cite book |first=Jonas |last=Močkus |title=Optimization Techniques IFIP Technical Conference Novosibirsk, July 1–7, 1974 |chapter=On bayesian methods for seeking the extremum |doi=10.1007/3-540-07165-2_55 |title=Optimization Techniques |series=Lecture Notes in Computer Science |date=1975 |volume=27 |pages=400–404 |isbn=978-3-540-07165-5 |doi-access=free }}</ref><ref>{{cite journal |first=Jonas |last=Močkus |title=On Bayesian Methods for Seeking the Extremum and their Application |journal=IFIP Congress |year=1977 |pages=195–200 }}</ref><ref name="Mockus1989">{{cite book |first=J. |last=Močkus |title=Bayesian Approach to Global Optimization |publisher=Kluwer Academic |___location=Dordrecht |year=1989 |isbn=0-7923-0115-3 }}</ref>

==Strategy==

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The approach has been applied to solve a wide range of problems,<ref>Eric Brochu, Vlad M. Cora, Nando de Freitas: [https://arxiv.org/abs/1012.2599 A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning]. CoRR abs/1012.2599 (2010)</ref> including [[learning to rank]],<ref>Eric Brochu, Nando de Freitas, Abhijeet Ghosh: [http://papers.nips.cc/paper/3219-active-preference-learning-with-discrete-choice-data.pdf Active Preference Learning with Discrete Choice Data]. Advances in Neural Information Processing Systems: 409-416 (2007)</ref> [[computer graphics]] and visual design,<ref>Eric Brochu, Tyson Brochu, Nando de Freitas: [http://haikufactory.com/files/sca2010.pdf A Bayesian Interactive Optimization Approach to Procedural Animation Design]. Symposium on Computer Animation 2010: 103–112</ref><ref>Yuki Koyama, Issei Sato, Daisuke Sakamoto, Takeo Igarashi: [https://koyama.xyz/project/sequential_line_search/download/preprint.pdf 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</ref><ref>Yuki Koyama, Issei Sato, Masataka Goto: [https://arxiv.org/abs/2005.04107 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</ref> [[robotics]],<ref>Daniel J. Lizotte, Tao Wang, Michael H. Bowling, Dale Schuurmans: [https://www.aaai.org/Papers/IJCAI/2007/IJCAI07-152.pdf Automatic Gait Optimization with Gaussian Process Regression]. International Joint Conference on Artificial Intelligence: 944–949 (2007)</ref><ref>Ruben Martinez-Cantin, Nando de Freitas, Eric Brochu, Jose Castellanos and Arnaud Doucet. [https://link.springer.com/article/10.1007%2Fs10514-009-9130-2# 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)</ref><ref>Scott Kuindersma, Roderic Grupen, and Andrew Barto. [http://ijr.sagepub.com/content/32/7/806.abstract# Variable Risk Control via Stochastic Optimization]. International Journal of Robotics Research, volume 32, number 7, pp 806–825 (2013)</ref><ref>Roberto Calandra, André Seyfarth, Jan Peters, and Marc P. Deisenroth [https://link.springer.com/article/10.1007%2Fs10472-015-9463-9 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</ref> [[sensor networks]],<ref>Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias W. Seeger: [https://infoscience.epfl.ch/record/177246/files/srinivas_ieeeit2012.pdf Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting]. IEEE Transactions on Information Theory 58(5):3250–3265 (2012)</ref><ref>Roman Garnett, Michael A. Osborne, Stephen J. Roberts: [http://www.academia.edu/download/30681076/ipsn673-garnett.pdf Bayesian optimization for sensor set selection]{{dead link|date=July 2022|bot=medic}}{{cbignore|bot=medic}}. ACM/IEEE International Conference on Information Processing in Sensor Networks: 209–219 (2010)</ref> automatic algorithm configuration,<ref>Frank Hutter, Holger Hoos, and Kevin Leyton-Brown (2011). [http://www.cs.ubc.ca/labs/beta/Projects/SMAC/papers/11-LION5-SMAC.pdf Sequential model-based optimization for general algorithm configuration], Learning and Intelligent Optimization</ref><ref>J. Snoek, H. Larochelle, R. P. Adams [https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf Practical Bayesian Optimization of Machine Learning Algorithms]. Advances in Neural Information Processing Systems: 2951-2959 (2012)</ref> [[Automated machine learning|automatic machine learning]] toolboxes,<ref>J. Bergstra, D. Yamins, D. D. Cox (2013).

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.704.3494&rep=rep1&type=pdf Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms].

Proc. SciPy 2013.</ref><ref>Chris Thornton, Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown: [https://arxiv.org/abs/1208.3719 Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms]. KDD 2013: 847–855</ref><ref>Jasper Snoek, Hugo Larochelle and Ryan Prescott Adams. [https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf Practical Bayesian Optimization of Machine Learning Algorithms]. Advances in Neural Information Processing Systems, 2012</ref> [[reinforcement learning]],<ref>{{Cite thesis |title=Safe Exploration in Reinforcement Learning: Theory and Applications in Robotics |url=https://www.research-collection.ethz.ch/handle/20.500.11850/370833 |publisher=ETH Zurich |date=2019 |degree=Doctoral Thesis |doi=10.3929/ethz-b-000370833 |language=en |first=Felix |last=Berkenkamp|hdl=20.500.11850/370833 }}</ref> planning, visual attention, architecture configuration in [[deep learning]], static program analysis, experimental [[particle physics]],<ref>Philip Ilten, Mike Williams, Yunjie Yang. [https://arxiv.org/abs/1610.08328 Event generator tuning using Bayesian optimization]. 2017 JINST 12 P04028. DOI: 10.1088/1748-0221/12/04/P04028</ref><ref>Evaristo Cisbani et al. [https://iopscience.iop.org/article/10.1088/1748-0221/15/05/P05009 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</ref> quality-diversity optimization,<ref>{{Cite arXiv |last1=Kent |first1=Paul |last2=Gaier |first2=Adam |last3=Mouret |first3=Jean-Baptiste |last4=Branke |first4=Juergen |date=2023-07-19 |title=BOP-Elites, a Bayesian Optimisation Approach to Quality Diversity Search with Black-Box descriptor functions |class=math.OC |publication-place=preprint: Arxiv |eprint=2307.09326}} Preprint: Arxiv.</ref><ref>{{Cite book |last1=Kent |first1=Paul |last2=Branke |first2=Juergen |title=Proceedings of the Genetic and Evolutionary Computation Conference |chapter=Bayesian Quality Diversity Search with Interactive Illumination |date=2023-07-12 |chapter-url=https://dl.acm.org/doi/10.1145/3583131.3590486 |series=GECCO '23 |___location=New York, NY, USA |publisher=Association for Computing Machinery |pages=1019–1026 |doi=10.1145/3583131.3590486 |isbn=979-8-4007-0119-1|s2cid=259833672 }}</ref><ref>{{Cite journal |last1=Gaier |first1=Adam |last2=Asteroth |first2=Alexander |last3=Mouret |first3=Jean-Baptiste |date=2018-09-01 |title=Data-Efficient Design Exploration through Surrogate-Assisted Illumination |url=http://dx.doi.org/10.1162/evco_a_00231 |journal=Evolutionary Computation |volume=26 |issue=3 |pages=381–410 |doi=10.1162/evco_a_00231 |pmid=29883202 |s2cid=47003986 |issn=1063-6560}}</ref> chemistry, material design, and drug development.<ref name=":0" /><ref>Gomez-Bombarelli et al. [https://pubs.acs.org/doi/10.1021/acscentsci.7b00572 Automatic Chemical Design using a Data-Driven Continuous Representation of Molecules]. ACS Central Science, Volume 4, Issue 2, 268-276 (2018)</ref><ref>Griffiths et al. [https://pubs.rsc.org/en/content/articlehtml/2020/sc/c9sc04026a Constrained Bayesian Optimization for Automatic Chemical Design using Variational Autoencoders] Chemical Science: 11, 577-586 (2020)</ref>