Team tao

Members
Overall Objectives
Scientific Foundations
Application Domains
Software
New Results
Contracts and Grants with Industry
Other Grants and Activities
Dissemination
Bibliography

Bibliography

Publications of the year

Doctoral dissertations and Habilitation theses

[1]
C. Kavka.
Evolutionary Design of Geometric-Based Fuzzy Systems, Ph. D. Thesis, Université Paris-Sud, 2006
http://tel.archives-ouvertes.fr/tel-00118883/en/.
[2]
V. P. Singh.
Automatic Seismic Velocity Inversion using Multi-Objective Evolutionary Algorithms, Ph. D. Thesis, École des Mines de Paris, 2006
http://tel.archives-ouvertes.fr/tel-00120310/en/.

Articles in refereed journals and book chapters

[3]
A. Auger, P. Chatelain, K. P..
R-leaping: Accelerating the stochastic simulation algorithm by reaction leaps, in: J. Chem. Phys., 2006, vol. 125.
[4]
N. Bredeche, Z. Shi, J.-D. Zucker.
Perceptual Learning and Abstraction in Machine Learning : an application to autonomous robotics, in: IEEE Transactions on Systems, Man and Cybernetics, part C, 2006, vol. 36, no 2, p. 172-181
http://hal.inria.fr/inria-00116923/en/.
[5]
A. Cornuéjols.
Machine Learning: The Necessity of Order (is order in order?), in: In order to learn: How the sequences of topics affect learning, F. Ritter, J. Nerb, E. Lehtinen, T. O'Shea (editors), Oxford University Press, 2006
http://hal.inria.fr/inria-00119757/en/.
[6]
S. Gelly, O. Teytaud, N. Bredeche, M. Schoenauer.
Universal Consistency and Bloat in GP, in: Revue d'Intelligence Artificielle, 2006, vol. 20, no 6, p. 805-827
http://hal.inria.fr/inria-00112840/en/.
[7]
S. Gelly, O. Teytaud.
Bayesian Networks: a Non-Frequentist Approach for Parametrization, and a more Accurate Structural Complexity Measure, in: Revue d'Intelligence Artificielle, 2006, vol. 20, no 6, p. 717-755
http://hal.inria.fr/inria-00112838/en/.
[8]
C. Germain-Renaud, C. Loomis, J. Moscicki, R. Texier.
Scheduling for Responsive Grids, in: Journal of Grid Computing, 2006
http://hal.inria.fr/inria-00117486/en/.
[9]
Y. Guermeur, O. Teytaud.
Estimation et contrôle des performances en généralisation des réseaux de neurones, in: Younes Bennani, ed., Apprentissage Connexioniste, Hermès, 2006.
[10]
R. Leriche, M. Schoenauer, M. Sebag.
Un état des lieux de l'optimisation évolutionnaire et de ses implications en sciences pour l'ingénieur, in: Modélisation Numérique: défis et perspectives, , Traité Mécanique et Ingénierie des Matériaux, P. Breitkopf, C. Knopf-Lenoir (editors), Hermès, 2006
http://hal.inria.fr/inria-00120733/en/.
[11]
A. Moreau, O. Teytaud, J.-P. Bertoglio.
Optimal estimation for Large-Eddy Simulation of turbulence and application to the analysis of subgrid models, in: Physics of fluids, 2006, vol. 18.
[12]
M. Schoenauer.
 , in: Optimisation évolutionnaire, in G. Allaire : Conception optimale de structures, Mathématiques et Applications, Springer Verlag, 2006, no 58, p. 221-264.
[13]
M. Sebag.
Fouille de donnée, in: Paradigmes et enjeux de l'informatique, N. Bidoit, L. F. del Cerro, S. Fdida, B. Vallée (editors), Hermès, 2006, p. 137-156.

Publications in Conferences and Workshops

[14]
N. Baskiotis, M. Sebag, M.-C. Gaudel, S.-D. Gouraud.
A Machine Learning approach for Statistical Software Testing, in: Twentieth International Joint Conference on Artificial Intelligence, Hyderabad, India, 2006
http://hal.inria.fr/inria-00112681/en/.
[15]
N. Baskiotis, M. Sebag, M.-C. Gaudel, S.-D. Gouraud.
EXIST: Exploitation/Exploration Inference for Statistical Software Testing, in: On-line Trading of Exploration and Exploitation, NIPS 2006 Workshop, Whistler, BC, Canada, 2006
http://hal.inria.fr/inria-00117172/en/.
[16]
A. Cornuéjols, F. Thollard.
Artificial data and language theory, in: GI workshop 2006. Grammatical inference: workshop on open problems and new directions, 21/11/2005, Saint-Etienne, France, Colin de la Higuera, 2006
http://hal.inria.fr/inria-00119756/en/.
[17]
A. Devert, N. Brédeche, M. Schoenauer.
BlindBuilder : a new encoding to evolve Lego-like structures, in: EUROGP 2006, Budapest, Hungary, Lecture Notes in Computer Science, 2006, vol. 3905, p. 61–72
http://hal.inria.fr/inria-00000995/en/.
[18]
A. Devert, N. Bredeche, M. Schoenauer.
Evolutionary Design of Buildable Objects with BlindBuilder : an Empirical Study, in: Asia-Pacific Workshop on Genetic Programming, Hanoi, Vietnam, Proceedings of the Third Asian-Pacific workshop on Genetic Programming, The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen, 2006, p. 98–109
http://hal.inria.fr/inria-00118652/en/.
[19]
C. Gagné, M. Schoenauer, M. Parizeau, M. Tomassini.
Genetic Programming, Validation Sets, and Parsimony Pressure, in: EuroGP 2006, Budapest, Hongrie, P. C. et al. (editor), Lecture Notes in Computer Science, Springer Verlag, 2006, vol. 3905, p. 109-120
http://hal.inria.fr/inria-00000996/en/.
[20]
C. Gagné, M. Schoenauer, M. Sebag, M. Tomassini.
Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection, in: Parallel Problem Solving from Nature, Reykjavik, T. R. et al. (editor), LNCS, Springer Verlag, 2006, no 4193, p. 1008-1017
http://hal.inria.fr/inria-00116344/en/.
[21]
S. Gelly, J. Mary, O. Teytaud.
Learning for stochastic dynamic programming, in: 11th European Symposium on Artificial Neural Networks (ESANN), bruges Belgium, 2006
http://hal.inria.fr/inria-00112796/en/.
[22]
S. Gelly, J. Mary, O. Teytaud.
On the ultimate convergence rates for isotropic algorithms and the best choices among various forms of isotropy, in: Parallel Problem Solving from Nature, Reykjavik, LNCS, 2006, no 4193, p. 32-41
http://hal.inria.fr/inria-00112816/en/.
[23]
S. Gelly, S. Ruette, O. Teytaud.
Comparison-based algorithms: worst-case optimality, optimality w.r.t a bayesian prior, the intraclass-variance minimization in EDA, and implementations with billiards, in: Parallel Problem Solving from Nature BTP-Workshop, Reykjavik, 2006
http://hal.inria.fr/inria-00112813/en/.
[24]
S. Gelly, O. Teytaud, C. Cagne.
Resource-Aware Parameterizations of EDA, in: Congress on Evolutionary Computation, Vancouver, BC, Canada, 2006
http://hal.inria.fr/inria-00112803/en/.
[25]
S. Gelly, O. Teytaud.
OpenDP a free Reinforcement Learning toolbox for discrete time control problems, in: NIPS Workshop on Machine Learning Open Source Software, Whistler (B.C.), 2006
http://hal.inria.fr/inria-00117392/en/.
[26]
C. Germain-Renaud.
Scheduling for Interactive Grids, in: First EGEE User Forum, Genève/Suisse, 2006
http://hal.inria.fr/inria-00117492/en/.
[27]
C. Germain-Renaud, C. Loomis, R. Texier, A. Osorio.
Grid Scheduling for Interactive Analysis, in: HealthGrid 2006, Valencia/Spain, in: Studies in Health Technology and Informatics, Challenges and Opportunities of Health Grids, IOS Press, 2006, vol. 120, p. 25-33
http://hal.inria.fr/inria-00117491/en/.
[28]
C. Hartland, N. Bredeche.
Evolutionary Robotics: From Simulation to the Real World using Anticipation, in: ABIALS, Rome/Italie, Oui, 2006
http://hal.inria.fr/inria-00120115/en/.
[29]
C. Hartland, N. Bredèche.
Evolutionary Robotics, Anticipation and the Reality Gap, in: ROBIO, Kunming/Chine, Oui, 2006
http://hal.inria.fr/inria-00120116/en/.
[30]
T. Heitz, J. Azé, M. Roche, A. Mela, P. Peinl, M. Amar Djalil.
Présentation de DEFT 06 (DÉfi Fouille de Textes), in: Atelier DEFT'06 - SDN'06 (Semaine du Document Numérique), Fribourg, Suisse, Actes de l'atelier DEFT'06, SDN'06 (Semaine du Document Numérique), 2006, p. 1-10
http://hal.inria.fr/inria-00119612/en/.
[31]
T. Heitz.
Modélisation du prétraitement des textes, in: JADT'06 (International Conference on Statistical Analysis of Textual Data), Besançon, France, Proceedings of JADT'06, 2006, vol. 1, p. 499-506
http://hal.inria.fr/inria-00119608/en/.
[32]
L. Hugues, N. Bredeche.
Simbad : an Autonomous Robot Simulation Package for Education and Research, in: Simulation of Adaptive Behavior (SAB 2006), Rome, Italy, 2006
http://hal.inria.fr/inria-00116929/en/.
[33]
V. Krmicek, M. Sebag.
Functional Brain Imaging with Multi-Objective Multi-Modal Evolutionary Optimization, in: Parallel Problem Solving from Nature, Reykjavik, T. R. et al. (editor), LNCS, Springer Verlag, 2006, no 4193, p. 382-391
http://hal.inria.fr/inria-00116342/en/.
[34]
S. Lallich, O. Teytaud, E. Prudhomme.
Association rules interestingness: measure and validation, in: Quality Measures in Data Mining, F. Guillet, H.-J. Hamilton (editors), Springer, 2006, 23 p.
[35]
S. Lallich, O. Teytaud, E. Prudhomme.
Statistical inference and data mining: false discoveries control, in: proceedings of the 17th COMPSTAT Symposium of the IASC, 2006.
[36]
M. Schoenauer, P. Savéant, V. Vidal.
Divide-and-Evolve : une nouvelle méta-heuristique pour la planification temporelle indépendante du domaine, in: Journées Francophones Planification, Décision, Apprentissage, Toulouse, F. Garcia, G. Verfaillie (editors), GDR I3 groupe PDMIA, 2006
http://hal.inria.fr/inria-00121779/en/.
[37]
M. Schoenauer, P. Savéant, V. Vidal.
Divide-and-Evolve: a New Memetic Scheme for Domain-Independent Temporal Planning, in: EvoCOP2006, Budapest, J. Gottlieb, G. Raidl (editors), LNCS, Springer Verlag, 2006, no 3906, p. 247-260
http://hal.inria.fr/inria-00000975/en/.
[38]
Y. Semet, M. Schoenauer.
On the Benefits of Inoculation, an Example in Train Scheduling, in: GECCO-2006, Seattle, M. C. et al. (editor), ACM Press, 2006
http://hal.inria.fr/inria-00116345/en/.
[39]
O. Teytaud, S. Gelly.
General lower bounds for evolutionary algorithms, in: Parallel Problem Solving from Nature, Reykjavik, LNCS, 2006, no 4193, p. 21-31
http://hal.inria.fr/inria-00112820/en/.
[40]
O. Teytaud.
How entropy-theorems can show that approximating high-dim Pareto-fronts is too hard, in: Bridging the Gap between Theory and Practice - Workshop PPSN-BTP, 2006.
[41]
O. Teytaud.
Why Simulation-Based Approachs with Combined Fitness are a Good Approach for Mining Spaces of Turing-equivalent Functions, in: Proc. of the IEEE Congress on Evolutionary Computation (CEC 2006), 2006.

Internal Reports

[42]
S. Gelly, Y. Wang, R. Munos, O. Teytaud.
Modification of UCT with Patterns in Monte-Carlo Go, Rapport de recherche INRIA, 2006, no RR-6062
http://hal.inria.fr/inria-00117266/en/.
[43]
D. Tessier, M. Schoenauer, C. Biernacki, G. Celeux, G. Govaert.
Evolutionary Latent Class Clustering of Qualitative Data, Rapport de recherche INRIA, 2006, no RR-6082
http://hal.inria.fr/inria-00122088/en/.

Miscellaneous

[44]
M. Amil, C. Gagné, N. Bredèche, S. Gelly, M. Schoenauer, O. Teytaud.
How to ensure universal consistency and no bloat with VC-dimensionDagstuhl Seminar "Theory of Evolutionary Algorithms", 06061, 2006.
[45]
S. Gelly, Y. Wang.
Exploration-Exploitation in Go: UCT for Monte-Carlo-Go. On-line trading of Exploration and Exploitation Workshop, NIPS Conference, 2006.
[46]
C. Hartland, S. Gelly, N. Baskiotis, O. Teytaud, M. Sebag.
Multi-armed Bandit, Dynamic Environments and Meta-BanditsOnline Trading of Exploration and Exploitation Workshop, NIPS, 2006
http://hal.archives-ouvertes.fr/hal-00113668/en/.
[47]
O. Teytaud, S. Gelly, S. Lallich, E. Prudhomme.
Quasi-random resamplings, with applications to rule extraction, cross-validation and (su-)bagging. Pascal Workshop IIIA'2006, 2006.

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