publication . Preprint . 2016

Interactive Preference Learning of Utility Functions for Multi-Objective Optimization

Dewancker, Ian; McCourt, Michael; Ainsworth, Samuel;
Open Access English
  • Published: 13 Dec 2016
Comment: 7 pages of text, 1 page of references, 3 figures, 1 algorithm, 1 table
free text keywords: Mathematics - Optimization and Control, 90C29, 90B50
Download from
16 references, page 1 of 2

[1] Valerie Belton, Jürgen Branke, Petri Eskelinen, Salvatore Greco, Julian Molina, Francisco Ruiz, and Roman Slowin´ski. Multiobjective optimization. chapter Interactive Multiobjective Optimization from a Learning Perspective, pages 405-433. Springer-Verlag, Berlin, Heidelberg, 2008. [OpenAIRE]

[2] James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyper-parameter optimization. In Advances in Neural Information Processing Systems, pages 2546-2554.

[3] Eric Brochu, Tyson Brochu, and Nando de Freitas. A bayesian interactive optimization approach to procedural animation design. In Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pages 103-112. Eurographics Association, 2010.

[4] Wei Chu and Zoubin Ghahramani. Preference learning with gaussian processes. In Proceedings of the 22nd international conference on Machine learning, pages 137-144. ACM, 2005. [OpenAIRE]

[5] Brochu Eric, Nando D Freitas, and Abhijeet Ghosh. Active preference learning with discrete choice data. In Advances in neural information processing systems, pages 409-416, 2008.

[6] M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, and F. Hutter. Efficient and robust automated machine leraning. In Advances in Neural Information Processing Systems 28, pages 2944-2952, December 2015.

[7] Jonathan E Fieldsend. Optimizing decision trees using multi-objective particle swarm optimization. In Swarm intelligence for multi-objective problems in data mining, pages 93-114. Springer, 2009. [OpenAIRE]

[8] Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, and Ryan P Adams. Predictive entropy search for multi-objective bayesian optimization. arXiv preprint arXiv:1511.05467, 2015.

[9] Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In Learning and Intelligent Optimization, pages 507-523. Springer, 2011.

[10] C. J. Van Rijsbergen. Information Retrieval. Butterworth-Heinemann, Newton, MA, USA, 2nd edition, 1979.

[11] Burr Settles. Active learning literature survey. University of Wisconsin, Madison, 52(55-66):11, 2010.

[12] Amar Shah and Zoubin Ghahramani. Pareto frontier learning with expensive correlated objectives. In Proceedings of The 33rd International Conference on Machine Learning, pages 1919-1927, 2016.

[13] Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas. Taking the human out of the loop: A review of bayesian optimization. Technical report, Universities of Harvard, Oxford, Toronto, and Google DeepMind, 2015.

[14] Jasper Snoek, Hugo Larochelle, and Ryan P Adams. Practical bayesian optimization of machine learning algorithms. In Advances in neural information processing systems, pages 2951-2959, 2012.

[15] Jasper Snoek, Kevin Swersky, Richard S Zemel, and Ryan P Adams. Input warping for bayesian optimization of non-stationary functions. In ICML, pages 1674-1682, 2014. [OpenAIRE]

16 references, page 1 of 2
Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue