Observation of Electroweak Single Top-Quark Production, Physical Review Letters, vol.10, issue.9, 2009. ,
DOI : 10.1016/j.physletb.2008.07.018
URL : https://hal.archives-ouvertes.fr/in2p3-00366602
A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012, p.2599, 2010. ,
Bayesian optimization for learning gaits under uncertainty, Annals of Mathematics and Artificial Intelligence, vol.7, issue.1-2, pp.1-2, 2016. ,
DOI : 10.1088/1748-3182/7/3/036005
URL : http://spiral.imperial.ac.uk/bitstream/10044/1/24167/2/AMAI.pdf
Black-box data-efficient policy search for robotics, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017. ,
DOI : 10.1109/IROS.2017.8202137
URL : https://hal.archives-ouvertes.fr/hal-01576683
Evolving three-dimensional objects with a generative encoding inspired by developmental biology, ECAL, pp.141-148, 2011. ,
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents, 2017. ,
Robots that can adapt like animals, Nature, vol.26, issue.7553, 2015. ,
DOI : 10.1038/nrn2332
URL : https://hal.archives-ouvertes.fr/hal-01158243
Evolutionary Robotics: What, Why, and Where to, Frontiers in Robotics and AI, 2015. ,
DOI : 10.1177/1059712302010003003
URL : https://hal.archives-ouvertes.fr/hal-01131267
Recent advances in surrogate-based optimization, Progress in Aerospace Sciences, 2009. ,
DOI : 10.1016/j.paerosci.2008.11.001
URL : https://eprints.soton.ac.uk/65935/1/Forr_09.pdf
Data-efficient exploration, optimization, and modeling of diverse designs through surrogate-assisted illumination, Proceedings of the Genetic and Evolutionary Computation Conference on , GECCO '17, 2017. ,
DOI : 10.1109/CVPR.2015.7298640
URL : https://hal.archives-ouvertes.fr/hal-01518698
A comprehensive survey of fitness approximation in evolutionary computation, Soft Computing, vol.9, issue.1, 2005. ,
DOI : 10.1007/s00500-003-0328-5
Exploiting open-endedness to solve problems through the search for novelty, Proc. of ALIFE, pp.329-336, 2008. ,
Abandoning Objectives: Evolution Through the Search for Novelty Alone, Evolutionary Computation, vol.7, issue.3, pp.189-223, 2011. ,
DOI : 10.1016/0165-6074(93)90215-7
Fast Suboptimal Algorithms for the Computation of Graph Edit Distance, Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), 2006. ,
DOI : 10.1007/11815921_17
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.427-436, 2015. ,
DOI : 10.1109/CVPR.2015.7298640
URL : http://yosinski.com/media/papers/Nguyen__2014__arXiv__Deep_Neural_Networks_are_Easily_Fooled.pdf
Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy Search, Proc. of ICRA, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01768279
How the body shapes the way we think: a new view of intelligence, 2006. ,
Variational Bayesian learning of nonlinear hidden state-space models for model predictive control, Neurocomputing, vol.72, issue.16-18, 2009. ,
DOI : 10.1016/j.neucom.2009.06.009
Gaussian Process for Machine Learning, Gaussian Process for Machine Learning, 2006. ,
Approximate graph edit distance computation by means of bipartite graph matching, Image and Vision Computing, vol.27, issue.7, 2009. ,
DOI : 10.1016/j.imavis.2008.04.004
Evolution strategies as a scalable alternative to reinforcement learning, 2017. ,
A distance measure between attributed relational graphs for pattern recognition, IEEE Transactions on Systems, Man, and Cybernetics, vol.13, issue.3, pp.353-362, 1983. ,
DOI : 10.1109/TSMC.1983.6313167
Picbreeder, Proceeding of the twenty-sixth annual CHI conference on Human factors in computing systems , CHI '08, pp.1759-1768, 2008. ,
DOI : 10.1145/1357054.1357328
Taking the Human Out of the Loop: A Review of Bayesian Optimization, Proc. IEEE, 2016. ,
DOI : 10.1109/JPROC.2015.2494218
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design, Proceedings of the 27th International Conference on Machine Learning (ICML), 2010. ,
Compositional pattern producing networks: A novel abstraction of development. Genetic programming and evolvable machines, 2007. ,
Evolving neural network agents in the NERO video game, Proc. IEEE, pp.182-189, 2005. ,
A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks, Artificial Life, vol.21, issue.2, 2009. ,
DOI : 10.1109/5.784219
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning, 2017. ,
Learning to control a 6-degree-of-freedom walking robot Computer as a Tool, EUROCON: The International Conference on, 2007. ,
Evolutionary function approximation for reinforcement learning, Journal of Machine Learning Research, vol.7, pp.877-917, 2006. ,
On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent, 2017. ,