Optimisation bayésienne (French Wikipedia)
Analysis of information sources in references of the Wikipedia article "Optimisation bayésienne" in French language version.
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aaai.org (Global: 9,352nd place; French: low place)
- Daniel J. Lizotte, Tao Wang, Michael H. Bowling, Dale Schuurmans: Automatic Gait Optimization with Gaussian Process Regression« https://web.archive.org/web/20170812175417/http://www.aaai.org/Papers/IJCAI/2007/IJCAI07-152.pdf »(Archive.org • Wikiwix • Google • Que faire ?),
acs.org (Global: 1,248th place; French: 778th place)
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)
arxiv.org (Global: 69th place; French: 232nd place)
- Jasper Snoek, « Practical Bayesian Optimization of Machine Learning Algorithms », Advances in Neural Information Processing Systems 25 (NIPS 2012), vol. 25, (arXiv 1206.2944, lire en ligne)
- Aaron Klein, « Fast bayesian optimization of machine learning hyperparameters on large datasets », Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR, , p. 528–536 (arXiv 1605.07079, lire en ligne)
- (en) Peter I. Frazier, « A Tutorial on Bayesian Optimization », .
- 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
- (en) Paul Kent, Adam Gaier, Jean-Baptiste Mouret et Juergen Branke, « BOP-Elites, a Bayesian Optimisation Approach to Quality Diversity Search with Black-Box descriptor functions », . Preprint: Arxiv.
- Adam Gaier, Alexander Asteroth et Jean-Baptiste Mouret, « Data-Efficient Design Exploration through Surrogate-Assisted Illumination », Evolutionary Computation, vol. 26, no 3, , p. 381–410 (ISSN 1063-6560, PMID 29883202, DOI 10.1162/evco_a_00231
, arXiv 1806.05865, S2CID 47003986)
bayesoptbook.com (Global: low place; French: low place)
- Roman Garnett, Bayesian Optimization, Cambridge University Press, (ISBN 978-1-108-42578-0, lire en ligne)
doi.org (Global: 2nd place; French: 3rd place)
dx.doi.org
- Jonas Močkus, Optimization Techniques IFIP Technical Conference Novosibirsk, July 1–7, 1974, vol. 27, coll. « Lecture Notes in Computer Science », , 400–404 p. (ISBN 978-3-540-07165-5, DOI 10.1007/3-540-07165-2_55 ), « On bayesian methods for seeking the extremum »
- Roman Garnett, Michael A. Osborne et Stephen J. Roberts « Proceedings of the 9th International Conference on Information Processing in Sensor Networks, IPSN 2010, April 12–16, 2010, Stockholm, Sweden » () (DOI 10.1145/1791212.1791238)
- (en) Felix Berkenkamp, Safe Exploration in Reinforcement Learning: Theory and Applications in Robotics (thèse), ETH Zurich, (DOI 10.3929/ethz-b-000370833, hdl 20.500.11850/370833, lire en ligne)
- Paul Kent et Juergen Branke, Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA, Association for Computing Machinery, coll. « GECCO '23 », , 1019–1026 p. (ISBN 979-8-4007-0119-1, DOI 10.1145/3583131.3590486, S2CID 259833672, lire en ligne), « Bayesian Quality Diversity Search with Interactive Illumination »
- Adam Gaier, Alexander Asteroth et Jean-Baptiste Mouret, « Data-Efficient Design Exploration through Surrogate-Assisted Illumination », Evolutionary Computation, vol. 26, no 3, , p. 381–410 (ISSN 1063-6560, PMID 29883202, DOI 10.1162/evco_a_00231 , arXiv 1806.05865, S2CID 47003986)
doi.org
- 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
epfl.ch (Global: 4,521st place; French: 1,512th place)
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 (Global: 2,224th place; French: 1,652nd place)
research-collection.ethz.ch
- (en) Felix Berkenkamp, Safe Exploration in Reinforcement Learning: Theory and Applications in Robotics (thèse), ETH Zurich, (DOI 10.3929/ethz-b-000370833, hdl 20.500.11850/370833, lire en ligne)
github.com (Global: 383rd place; French: 397th place)
- Samuel Wilson, ParBayesianOptimization R package, (lire en ligne)
googleusercontent.com (Global: 243rd place; French: 21st place)
webcache.googleusercontent.com
- Daniel J. Lizotte, Tao Wang, Michael H. Bowling, Dale Schuurmans: Automatic Gait Optimization with Gaussian Process Regression« https://web.archive.org/web/20170812175417/http://www.aaai.org/Papers/IJCAI/2007/IJCAI07-152.pdf »(Archive.org • Wikiwix • Google • Que faire ?),
haikufactory.com (Global: low place; French: low place)
- 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 (Global: 102nd place; French: 779th place)
hdl.handle.net
- (en) Felix Berkenkamp, Safe Exploration in Reinforcement Learning: Theory and Applications in Robotics (thèse), ETH Zurich, (DOI 10.3929/ethz-b-000370833, hdl 20.500.11850/370833, lire en ligne)
iop.org (Global: 1,725th place; French: 998th place)
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
issn.org (Global: 57th place; French: 4th place)
portal.issn.org
- Adam Gaier, Alexander Asteroth et Jean-Baptiste Mouret, « Data-Efficient Design Exploration through Surrogate-Assisted Illumination », Evolutionary Computation, vol. 26, no 3, , p. 381–410 (ISSN 1063-6560, PMID 29883202, DOI 10.1162/evco_a_00231 , arXiv 1806.05865, S2CID 47003986)
koyama.xyz (Global: low place; French: low place)
- 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 (Global: low place; French: low place)
proceedings.mlr.press
- Aaron Klein, « Fast bayesian optimization of machine learning hyperparameters on large datasets », Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR, , p. 528–536 (arXiv 1605.07079, lire en ligne)
neurips.cc (Global: low place; French: low place)
proceedings.neurips.cc
- Jasper Snoek, « Practical Bayesian Optimization of Machine Learning Algorithms », Advances in Neural Information Processing Systems 25 (NIPS 2012), vol. 25, (arXiv 1206.2944, lire en ligne)
nih.gov (Global: 4th place; French: 12th place)
ncbi.nlm.nih.gov
- Adam Gaier, Alexander Asteroth et Jean-Baptiste Mouret, « Data-Efficient Design Exploration through Surrogate-Assisted Illumination », Evolutionary Computation, vol. 26, no 3, , p. 381–410 (ISSN 1063-6560, PMID 29883202, DOI 10.1162/evco_a_00231 , arXiv 1806.05865, S2CID 47003986)
nips.cc (Global: low place; French: low place)
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 (Global: low place; French: low place)
- Hennig, P., Osborne, M. A. et Kersting, H. P., Probabilistic Numerics, Cambridge University Press, , 243–278 p. (ISBN 978-1107163447, lire en ligne)
psu.edu (Global: 207th place; French: 929th place)
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 (Global: 2,020th place; French: 1,755th place)
pubs.rsc.org
- Griffiths et al. Constrained Bayesian Optimization for Automatic Chemical Design using Variational Autoencoders Chemical Science: 11, 577-586 (2020)
sagepub.com (Global: 731st place; French: 771st place)
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 (Global: 11th place; French: 325th place)
api.semanticscholar.org
- Paul Kent et Juergen Branke, Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA, Association for Computing Machinery, coll. « GECCO '23 », , 1019–1026 p. (ISBN 979-8-4007-0119-1, DOI 10.1145/3583131.3590486, S2CID 259833672, lire en ligne), « Bayesian Quality Diversity Search with Interactive Illumination »
- Adam Gaier, Alexander Asteroth et Jean-Baptiste Mouret, « Data-Efficient Design Exploration through Surrogate-Assisted Illumination », Evolutionary Computation, vol. 26, no 3, , p. 381–410 (ISSN 1063-6560, PMID 29883202, DOI 10.1162/evco_a_00231 , arXiv 1806.05865, S2CID 47003986)
springer.com (Global: 274th place; French: 223rd place)
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 (Global: 1,997th place; French: 2,874th place)
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 (Global: 4,378th place; French: 7,665th place)
wrap.warwick.ac.uk
- Paul Kent et Juergen Branke, Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA, Association for Computing Machinery, coll. « GECCO '23 », , 1019–1026 p. (ISBN 979-8-4007-0119-1, DOI 10.1145/3583131.3590486, S2CID 259833672, lire en ligne), « Bayesian Quality Diversity Search with Interactive Illumination »
web.archive.org (Global: 1st place; French: 1st place)
- Daniel J. Lizotte, Tao Wang, Michael H. Bowling, Dale Schuurmans: Automatic Gait Optimization with Gaussian Process Regression« https://web.archive.org/web/20170812175417/http://www.aaai.org/Papers/IJCAI/2007/IJCAI07-152.pdf »(Archive.org • Wikiwix • Google • Que faire ?),
wikiwix.com (Global: 194th place; French: 17th place)
archive.wikiwix.com
- Daniel J. Lizotte, Tao Wang, Michael H. Bowling, Dale Schuurmans: Automatic Gait Optimization with Gaussian Process Regression« https://web.archive.org/web/20170812175417/http://www.aaai.org/Papers/IJCAI/2007/IJCAI07-152.pdf »(Archive.org • Wikiwix • Google • Que faire ?),