Collaborative hyperparameter tuning

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 2, 2013, Pages 858-866

  Bardenet, Rémi ^a,b,c   Brendel, Mátyás ^b,d   Kégl, Balázs ^b,c   Sebag, Michèle ^c  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITÉ PARIS SUD   (France)

^c CNRS   (France)

^d FERCHAU ENGINEERING GMBH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EXPERIMENTS; LEARNING ALGORITHMS; LEARNING SYSTEMS;

ALGORITHMIC APPROACH; COMPUTATIONAL POWER; COMPUTING INFRASTRUCTURES; DEEP BELIEF NETWORKS; HYPER-PARAMETER OPTIMIZATIONS; HYPER-PARAMETER SPACE; OPTIMIZATION TECHNIQUES; SURROGATE-BASED OPTIMIZATION;

OPTIMIZATION;

EID: 84882279850     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Benbouzid, D., Busa-Fekete, R., Casagrande, N., Collin, F.-D., and Kégl, B. MultiBoost: a multipurpose boosting package. Journal of Machine Learning Research, 13:549-553, 2012.
2. Bergstra, J. and Bengio, Y. Random search for hyperparameter optimization. Journal of Machine Learning Research, 2012.
3. Bergstra, J., Bardenet, R., Kégl, B., and Bengio, Y. Algorithms for hyperparameter optimization. In Advances in Neural Information Processing Systems (NIPS), volume 24. The MIT Press, 2011.
4. Brendel, M. and Schoenauer, M. Instance-based parameter tuning for evolutionary AI planning. In Proceedings of the 20th Genetic and Evolutionary Computation Conference, 2011.
5. Chu, W. and Ghahramani, Z. Preference learning with Gaussian processes. In Proceedings of the 22nd International Conference on Machine Learning, pp. 137-144, 2005.
6. Coates, A., Lee, H., and Ng, A. Y. An analysis of single-layer networks in unsupervised feature learning. In 14th International Conference on Artificial Intelligence and Statistics (AISTATS), 2011.
7. Demšar, J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7:1-30, 2006. (Pubitemid 43022939)
8. Hutter, F. Automated Configuration of Algorithms for Solving Hard Computational Problems. PhD thesis, University of British Columbia, 2009.
9. Hutter, F., Hoos, H. H., Leyton-Brown, K., and Stützle, T. ParamILS: an automatic algorithm configuration framework. Journal of Artificial Intelligence Research, 36:267-306, October 2009.
10. Joachims, T. Optimizing search engines using click-through data. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), 2002.
11. Jones, D. R. A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization, 21:345-383, 2001. (Pubitemid 33672374)
12. Kégl, B. and Busa-Fekete, R. Boosting products of base classifiers. In International Conference on Machine Learning, volume 26, pp. 497-504, Montreal, Canada, 2009.
13. Lacoste, A., Laviolette, F., and Marchand, M. Bayesian comparison of machine learning algorithms on single and multiple datasets. In 15th International Conference on Artificial Intelligence and Statistics (AISTATS), 2012.
14. Lizotte, D. Practical Bayesian Optimization. PhD thesis, University of Alberta, 2008.
15. Nannen, V. and Eiben, A. E. Relevance estimation and value calibration of evolutionary algorithm parameters. In Proceedings of the International Joint Conference on Artificial Intelligence, pp. 975-980, 2007.
16. Pinto, N., Doukhan, D., DiCarlo, J. J., and Cox, D. D. A high-throughput screening approach to discovering good forms of biologically inspired visual representation. PLoS Computational Biology, 5(11), 11 2009.
17. Rasmussen, C. E. and Williams, C. K. I. Gaussian Processes for Machine Learning. MIT Press, 2006.
18. Schapire, R. E. and Singer, Y. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297-336, 1999. (Pubitemid 32210620)
19. Snoek, J., Larochelle, H., and Adams, R. P. Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems, volume 25, 2012.
20. Srinivas, N., Krause, A., Kakade, S., and Seeger, M. Gaussian process optimization in the bandit setting: No regret and experimental design. In Proceedings of the 27th International Conference on Machine Learning, 2010.
21. Thornton, C., Hutter, F., Hoos, H. H., and Leyton-Brown, K. Auto-WEKA: Automated selection and hyper-parameter optimization of classification algorithms. Technical report, http://arxiv.org/abs/1208.3719, 2012.
22. Villemonteix, J., Vazquez, E., and Walter, E. An informational approach to the global optimization of expensive-to-evaluate functions. Journal of Global Optimization, 2006.