Members
Overall Objectives
Research Program
Application Domains
Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Dissemination
Bibliography
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PDF e-Pub


Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

[1]
L. Arnold.
Learning Deep Representations : Toward a better new understanding of the deep learning paradigm, Université Paris Sud - Paris XI, June 2013.
http://hal.inria.fr/tel-00842447
[2]
A. Couetoux.
Monte Carlo Tree Search pour les problèmes de décision séquentielle en milieu continus et stochastiques, Université Paris Sud - Paris XI, September 2013.
http://hal.inria.fr/tel-00927252
[3]
J.-B. Hoock.
Contributions to Simulation-based High-dimensional Sequential Decision Making, Université Paris Sud - Paris XI, April 2013.
http://hal.inria.fr/tel-00912338
[4]
I. Loshchilov.
Surrogate-Assisted Evolutionary Algorithms, Université Paris Sud - Paris XI and Institut national de recherche en informatique et en automatique - Inria, January 2013.
http://hal.inria.fr/tel-00823882
[5]
V. Martin.
Modélisation probabiliste et inférence par l'algorithme Belief Propagation, Ecole Nationale Supérieure des Mines de Paris, May 2013.
http://hal.inria.fr/tel-00867693
[6]
J.-m. Montanier.
Environment-driven Distributed Evolutionary Adaptation for Collective Robotic Systems, Université Paris Sud - Paris XI, March 2013.
http://hal.inria.fr/tel-00811496

Articles in International Peer-Reviewed Journals

[7]
J. Atif, C. Hudelot, I. Bloch.
Explanatory reasoning for image understanding using formal concept analysis and description logics, in: IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans: Systems and Humans, October 2013, pp. 1–19. [ DOI : 10.1109/TSMC.2013.2280440 ]
http://hal.inria.fr/hal-00862563
[8]
N. Brunel, Q. Clairon, F. D'Alché-Buc.
Parametric Estimation of Ordinary Differential Equations with Orthogonality Conditions, in: Journal of American Statistics Association, 2013, (to appear).
http://hal.inria.fr/hal-00867370
[9]
R. Busa-Fekete, B. Kégl, T. Elteto, G. Szarvas.
Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers, in: Machine Learning, 2013, vol. 93, pp. 261-292, ANR-2010-COSI-002. [ DOI : 10.1007/s10994-013-5360-9 ]
http://hal.inria.fr/in2p3-00869803
[10]
P. Caillou, J. Gil-Quijano.
Description automatique de dynamiques de groupes dans des simulations à base d'agents, in: Revue d'Intelligence Artificielle, January 2014, vol. 27, no 6.
http://hal.inria.fr/hal-00927587
[11]
P. Caillou, J. Gil-Quijano, X. Zhou.
Automated observation of multi-agent based simulations: a statistical analysis approach, in: Studia Informatica Universalis, 2013, to appear.
http://hal.inria.fr/hal-00738384
[12]
C.-W. Chou, P.-C. Chou, J.-J. Christophe, A. Couetoux, P. De Freminville, N. Galichet, C.-S. Lee, J. Liu, D. L. St-Pierre, M. Sebag, O. Teytaud, M.-H. Wang, L.-W. Wu, S.-J. Yen.
Strategic Choices in Optimization, in: Journal of Information Sciences and Engineering, 2013.
http://hal.inria.fr/hal-00863577
[13]
D. Feng, C. Germain-Renaud, T. Glatard.
Efficient Distributed Monitoring with Active Collaborative Prediction, in: Future Generation Computer Systems, 2013, vol. 29, no 8, pp. 2272-2283. [ DOI : 10.1016/j.future.2013.06.001 ]
http://hal.inria.fr/hal-00784038
[14]
C. Furtlehner.
Approximate Inverse Ising models close to a Bethe Reference Point, in: Journal of Statistical Mechanics: Theory and Experiment, September 2013, P09020 p. [ DOI : 10.1088/1742-5468/2013/09/P09020 ]
http://hal.inria.fr/hal-00865085
[15]
C. Furtlehner.
Pairwise MRF Models Selection for Traffic Inference, in: Interdisciplinary Information Sciences, August 2013, vol. 19, no 1, pp. 17-22. [ DOI : 10.4036/iis.2013.17 ]
http://hal.inria.fr/hal-00865089
[16]
G. Michailidis, F. D'Alché-Buc.
Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues, in: Mathematical Biosciences, December 2013, vol. 246, no 2, pp. 326–334. [ DOI : 10.1016/j.mbs.2013.10.003 ]
http://hal.inria.fr/hal-00909809
[17]
A. Muzy, F. Varenne, B. P. Zeigler, J. Caux, P. Coquillard, L. Touraille, D. Prunetti, P. Caillou, O. Michel, D. Hill.
Refounding of Activity Concept ? Towards a Federative Paradigm for Modeling and Simulation, in: Simulation, Transactions of the Society for Modeling and Simulation International, February 2013, vol. 89, no 2, pp. 156-177. [ DOI : 10.1177/0037549712457852 ]
http://hal.inria.fr/hal-00738218
[18]
O. Nempont, J. Atif, I. Bloch.
A constraint propagation approach to structural model based image segmentation and recognition, in: Information Sciences, October 2013, vol. 246, pp. 1-27.
http://hal.inria.fr/hal-00862455
[19]
W. Wang, M. Sebag.
Hypervolume indicator and dominance reward based multi-objective Monte-Carlo Tree Search, in: Machine Learning, May 2013, vol. 92, no 2-3, pp. 403-429. [ DOI : 10.1007/s10994-013-5369-0 ]
http://hal.inria.fr/hal-00852048
[20]
X. Zhang, C. Furtlehner, C. Germain-Renaud, M. Sebag.
Data Stream Clustering with Affinity Propagation, in: IEEE Transactions on Knowledge and Data Engineering, 2014.
http://hal.inria.fr/hal-00862941

International Conferences with Proceedings

[21]
O. Ait Elhara, A. Auger, N. Hansen.
A Median Success Rule for Non-Elitist Evolution Strategies : Study of Feasibility, in: Genetic and Evolutionary Computation Conference, Amsterdam, Netherlands, C. Blum, E. Alba (editors), ACM Press, March 2013, pp. 415-422.
http://hal.inria.fr/hal-00801414
[22]
Y. Akimoto, Y. Ollivier.
Objective Improvement in Information-Geometric Optimization, in: Foundations of Genetic Algorithms XII, Adelaide, Australia, January 2013.
http://hal.inria.fr/hal-00752489
[23]
S. Astete-Morales, J. Liu, O. Teytaud.
Noisy optimization convergence rates, in: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion, Amsterdam, Netherlands, ACM, 2013, pp. 223–224. [ DOI : 10.1145/2464576.2464687 ]
http://hal.inria.fr/hal-00863584
[24]
J. Atif, I. Bloch, F. Distel, C. Hudelot.
A fuzzy extension of explanatory relations based on mathematical morphology, in: EUSFLAT 2013, Milano, Italy, September 2013, pp. 244–351.
http://hal.inria.fr/hal-00862605
[25]
J. Atif, I. Bloch, F. Distel, C. Hudelot.
Mathematical morphology operators over concept lattices, in: ICFCA, Dresden, Germany, Springer, May 2013, vol. LNAI 7880, pp. 28–43.
http://hal.inria.fr/hal-00862621
[26]
A. Auger, D. Brockhoff, N. Hansen.
Benchmarking the Local Metamodel CMA-ES on the Noiseless BBOB'2013 Test Bed, in: GECCO (Companion), workshop on Black-Box Optimization Benchmarking (BBOB'2013), Amsterdam, Netherlands, July 2013.
http://hal.inria.fr/hal-00825840
[27]
D. Auger, A. Couetoux, O. Teytaud.
Continuous Upper Confidence Trees with Polynomial Exploration - Consistency, in: ECML/PKKD 2013, Prague, Czech Republic, H. Blockeel, K. Kersting, S. Nijssen, F. Železný (editors), LNCS, Springer Verlag, September 2013, vol. 8188, pp. 194-209.
http://hal.inria.fr/hal-00835352
[28]
R. Bardenet.
Monte Carlo methods, in: IN2P3 School of Statistics (SOS2012), Autrans, France, T. Delemontex, A. Lucotte (editors), 2013, vol. 55, ISBN:978-2-7598-1032-1. [ DOI : 10.1051/epjconf/20135502002 ]
http://hal.inria.fr/in2p3-00846142
[29]
R. Bardenet, M. Brendel, B. Kégl, M. Sebag.
Collaborative hyperparameter tuning, in: 30th International Conference on Machine Learning (ICML 2013), Atlanta, United States, S. Dasgupta, D. McAllester (editors), 2013, vol. 28, pp. 199-207.
http://hal.inria.fr/in2p3-00907381
[30]
I. Bloch, J. Atif.
Distance to bipolar information from morphological dilation, in: European Society for Fuzzy Logic and Technology (EUSFLAT), Milano, Italy, September 2013, pp. 266–273.
http://hal.inria.fr/hal-00862603
[31]
M. Bressan, E. Peserico, L. Pretto.
The power of local information in PageRank, in: International conference on World Wide Web (WWW), Rio de Janeiro, Brazil, 2013.
http://hal.inria.fr/hal-00862816
[32]
J. Decock, O. Teytaud.
Linear Convergence of Evolution Strategies with Derandomized Sampling Beyond Quasi-Convex Functions, in: EA - 11th Biennal International Conference on Artificial Evolution - 2013, Bordeaux, France, Lecture Notes in Computer Science, Springer, August 2013.
http://hal.inria.fr/hal-00907671
[33]
J. Decock, O. Teytaud.
Noisy Optimization Complexity Under Locality Assumption, in: FOGA - Foundations of Genetic Algorithms XII - 2013, Adelaide, Australia, February 2013.
http://hal.inria.fr/hal-00755663
[34]
A. Drogoul, E. Amouroux, P. Caillou, B. Gaudou, A. Grignard, N. Marilleau, P. Taillandier, M. Vavaseur, D.-A. Vo, J.-D. Zucker.
GAMA: A Spatially Explicit, Multi-level, Agent-Based Modeling and Simulation Platform, in: Practical Applications of Agents and Multi-Agent Systems, Spain, Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2013, vol. 7879, pp. 271-274.
http://hal.inria.fr/hal-00834494
[35]
A. Drogoul, E. Amouroux, P. Caillou, B. Gaudou, A. Grignard, N. Marilleau, P. Taillandier, M. Vavaseur, D.-A. Vo, J.-D. Zucker.
GAMA: multi-level and complex environment for agent-based models and simulations (demonstration), in: international conference on Autonomous agents and multi-agent systems, United States, 2013, pp. 1361-1362.
http://hal.inria.fr/hal-00834498
[36]
N. Galichet, M. Sebag, O. Teytaud.
Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits, in: Asian Conference on Machine Learning 2013, Canberra, Australia, C. S. Ong, T. B. Ho (editors), Journal of Machine Learning Research : Workshop and Conference Proceedings, November 2013, vol. 29, pp. 245-260.
http://hal.inria.fr/hal-00924062
[37]
Y. Isaac, Q. Barthélemy, C. Gouy-Pailler, J. Atif, M. Sebag.
Multi-dimensional sparse structured signal approximation using split bregman iterations, in: 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, IEEE, May 2013, pp. 3826-3830.
http://hal.inria.fr/hal-00862645
[38]
M. R. Khouadjia, M. Schoenauer, V. Vidal, J. Dréo, P. Savéant.
Multi-Objective AI Planning: Comparing Aggregation and Pareto Approaches, in: EvoCOP – 13th European Conference on Evolutionary Computation in Combinatorial Optimisation, Vienna, Austria, M. Middendorf, C. Blum (editors), LNCS, Springer Verlag, March 2013, vol. 7832, pp. 202-213.
http://hal.inria.fr/hal-00820634
[39]
M. R. Khouadjia, M. Schoenauer, V. Vidal, J. Dréo, P. Savéant.
Multi-Objective AI Planning: Evaluating DAE-YAHSP on a Tunable Benchmark, in: EMO'13 - 7th International Conference on Evolutionary Multi-Criterion Optimization, Sheffield, United Kingdom, R. C. Purshouse, P. J. Fleming, C. M. Fonseca (editors), LNCS, Springer Verlag, March 2013, vol. 7811, pp. 36-50.
http://hal.inria.fr/hal-00750560
[40]
M. R. Khouadjia, M. Schoenauer, V. Vidal, J. Dréo, P. Savéant.
Pareto-Based Multiobjective AI Planning, in: IJCAI 2013, Beijing, China, F. Rossi (editor), IJCAI/AAAI, August 2013.
http://hal.inria.fr/hal-00835003
[41]
M. R. Khouadjia, M. Schoenauer, V. Vidal, J. Dréo, P. Savéant.
Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning, in: LION7 - Learning and Intelligent OptimizatioN Conference, Catania, Italy, P. Pardalos, G. Nicosia (editors), LNCS, Springer Verlag, March 2013, To appear.
http://hal.inria.fr/hal-00820617
[42]
B. Kégl.
Introduction to multivariate discrimination, in: IN2P3 School of Statistics (SOS2012), Autrans, France, T. Delemontex, A. Lucotte (editors), 2013, vol. 55, ISBN:978-2-7598-1032-1. [ DOI : 10.1051/epjconf/20135502001 ]
http://hal.inria.fr/in2p3-00846125
[43]
I. Loshchilov, M. Schoenauer, M. Sebag.
BI-population CMA-ES Algorithms with Surrogate Models and Line Searches, in: Workshop Proceedings of the (GECCO) Genetic and Evolutionary Computation Conference, Amsterdam, Netherlands, ACM, April 2013, 8 p.
http://hal.inria.fr/hal-00818596
[44]
I. Loshchilov, M. Schoenauer, M. Sebag.
Intensive Surrogate Model Exploitation in Self-adaptive Surrogate-assisted CMA-ES (saACM-ES), in: Genetic and Evolutionary Computation Conference (GECCO 2013), Amsterdam, Netherlands, C. Blum, E. Alba (editors), April 2013, pp. 439-446.
http://hal.inria.fr/hal-00818595
[45]
I. Loshchilov, M. Schoenauer, M. Sebag.
KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization, in: Conférence sur l'Apprentissage Automatique, Lille, France, August 2013.
http://hal.inria.fr/hal-00851189
[46]
M. Loth, M. Sebag, Y. Hamadi, M. Schoenauer.
Bandit-based Search for Constraint Programming, in: International Conference on Principles and Practice of Constraint Programming, Uppsala, Sweden, C. Schulte (editor), LNCS, Springer Verlag, September 2013, vol. 8124, pp. 464-480.
http://hal.inria.fr/hal-00863451
[47]
M. Loth, M. Sebag, Y. Hamadi, M. Schoenauer, C. Schulte.
Hybridizing Constraint Programming and Monte-Carlo Tree Search: Application to the Job Shop problem, in: Learning And Intelligent Optimization Conference, Catania, Italy, January 2013.
http://hal.inria.fr/hal-00863453
[48]
G. Marceau, P. Savéant, M. Schoenauer.
Computational Methods for Probabilistic Inference of Sector Congestion in Air Traffic Management, in: Interdisciplinary Science for Innovative Air Traffic Management, Toulouse, France, July 2013.
http://hal.inria.fr/hal-00862243
[49]
G. Marceau, P. Savéant, M. Schoenauer.
Multiobjective Tactical Planning under Uncertainty for Air Traffic Flow and Capacity Management, in: IEEE Congress on Evolutionary Computation, Cancun, Mexico, C. A. C. Coello, Y. Jin (editors), IEEE Press, June 2013, pp. 1548-1555.
http://hal.inria.fr/hal-00862223
[50]
G. Marceau, P. Savéant, M. Schoenauer.
Strategic Planning in Air Traffic Control as a Multi-objective Stochastic Optimization Problem, in: ATM Seminar 2013, Chicago, United States, June 2013.
http://hal.inria.fr/hal-00862186
[51]
B. Romain, V. Letort, O. Lucidarme, L. Rouet, F. D'Alché-Buc.
A multi-task learning approach for compartmental model parameter estimation in DCE-CT sequences, in: 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013), Nagoya, Japan, Lecture Notes in Computer Science, September 2013, vol. 8150, pp. 271–278. [ DOI : 10.1007/978-3-642-40763-5_34 ]
http://hal.inria.fr/hal-00832184
[52]
B. Szorenyi, R. Busa-Fekete, I. Hegedüs, R. Ormandi, M. Jelasity, B. Kégl.
Gossip-based distributed stochastic bandit algorithms, in: 30th International Conference on Machine Learning (ICML 2013), Atlanta, United States, S. Dasgupta, D. McAllester (editors), 2013, vol. 28, pp. 19-27.
http://hal.inria.fr/in2p3-00907406

Conferences without Proceedings

[53]
R. Akrour, M. Schoenauer, M. Sebag.
Interactive Robot Education, in: ECML/PKDD Workshop on Reinforcement Learning with Generalized Feedback: Beyond Numeric Rewards, Berlin, Germany, J. Fuernkranz, E. Hüllermeier (editors), September 2013.
http://hal.inria.fr/hal-00931347
[54]
Y. Isaac, Q. Barthélemy, J. Atif, C. Gouy-Pailler, M. Sebag.
Régularisations spatiales pour la décomposition de signaux EEG sur un dictionnaire temps-fréquence, in: Colloque Gretsi XXIV, France, September 2013.
http://hal.inria.fr/hal-00862707
[55]
N. Lim, Y. Senbabaoglu, G. Michailidis, F. D'Alché-Buc.
Boosting an operator-valued kernel model and application to network inference, in: Workshop on Machine Learning for System Identification, Atlanta, United States, June 2013.
http://hal.inria.fr/hal-00844490

Scientific Books (or Scientific Book chapters)

[56]
N. Hansen, A. Auger.
Principled Design of Continuous Stochastic Search: From Theory to Practice, in: Theory and Principled Methods for the Design of Metaheuristics, Y. Borenstein, A. Moraglio (editors), Natural Computing Series, Springer, 2013.
http://hal.inria.fr/hal-00808450
[57]
Y. Ollivier.
A visual introduction to Riemannian curvatures and some discrete generalizations, in: Analysis and Geometry of Metric Measure Spaces: Lecture Notes of the 50th Séminaire de Mathématiques Supérieures (SMS), Montréal, 2011, G. Dafni, R. McCann, A. Stancu (editors), AMS, 2013, pp.  197-219.
http://hal.inria.fr/hal-00858008

Internal Reports

[58]
V. Martin, J.-M. Lasgouttes, C. Furtlehner.
Using Latent Binary Variables for Online Reconstruction of Large Scale Systems, Inria, December 2013, no RR-8435, 34 p.
http://hal.inria.fr/hal-00922106
[59]
M. Misir, M. Sebag.
Algorithm Selection as a Collaborative Filtering Problem, December 2013, 43 p.
http://hal.inria.fr/hal-00922840

Other Publications

[60]
L. Arnold, Y. Ollivier.
Layer-wise learning of deep generative models, 2013.
http://hal.inria.fr/hal-00794302
[61]
S. Astete-Morales, M.-L. Cauwet, A. Couetoux, J. Decock, J. Liu, O. Teytaud.
Noisy Optimization, in: Dagstuhl seminar 13271, Dagstuhl, Germany, 2013, Dagstuhl seminar 13271.
http://hal.inria.fr/hal-00844305
[62]
A. Auger, N. Hansen.
Linear Convergence on Positively Homogeneous Functions of a Comparison Based Step-Size Adaptive Randomized Search: the (1+1) ES with Generalized One-fifth Success Rule, October 2013.
http://hal.inria.fr/hal-00877161
[63]
A. Auger, N. Hansen.
On Proving Linear Convergence of Comparison-based Step-size Adaptive Randomized Search on Scaling-Invariant Functions via Stability of Markov Chains, November 2013.
http://hal.inria.fr/hal-00877160
[64]
N. Lim, F. D'Alché-Buc, C. Auliac, G. Michailidis.
Operator-valued Kernel-based Vector Autoregressive Models for Network Inference, October 2013.
http://hal.inria.fr/hal-00872342
[65]
Y. Ollivier.
Efficient Riemannian training of recurrent neural networks for learning symbolic data sequences, 2013, Preliminary version. 2nd version: recurrent tensor-square differential metric added, more thorough experiments, title changed.
http://hal.inria.fr/hal-00857980
[66]
Y. Ollivier.
Riemannian metrics for neural networks, 2013, (3rd version: tensor square derivative metric discussed).
http://hal.inria.fr/hal-00857982
References in notes
[67]
R. Akrour, M. Schoenauer, M. Sebag.
APRIL: Active Preference-learning based Reinforcement Learning, in: ECML PKDD 2012, Peter Flach et al (editor), LNCS, Springer Verlag, September 2012, vol. 7524, pp. 116-131.
[68]
D. Benbouzid, R. Busa-Fekete, N. Casagrande, F.-D. Collin, B. Kégl.
Multiboost: a multi-purpose boosting package, in: Journal of Machine Learning Research, 2012, vol. 13, pp. 549-553.
http://hal.inria.fr/in2p3-00698455
[69]
H.-G. Beyer.
Evolution Strategies, in: Scholarpedia, 2007, vol. 2, no 8, 1965 p. [ DOI : 10.4249/scholarpedia.1965 ]
http://www.scholarpedia.org/article/Evolution_strategies
[70]
J. Bibai, P. Savéant, M. Schoenauer, V. Vidal.
An Evolutionary Metaheuristic Based on State Decomposition for Domain-Independent Satisficing Planning, in: ICAPS 2010, R. Brafman, H. Geffner, J. Hoffmann, H. Kautz (editors), AAAI Press, May 2010, pp. 15-25.
http://hal.archives-ouvertes.fr/docs/00/45/62/92/PDF/icaps10.pdf
[71]
G. Fouquier, J. Atif, I. Bloch.
Sequential model-based segmentation and recognition of image structures driven by visual features and spatial relations, in: Computer Vision and Image Understanding, January 2012, vol. 116, no 1, pp. 146–165, Impact factor: 1,340.
[72]
N. Hansen, A. Ostermeier.
Completely Derandomized Self-Adaptation in Evolution Strategies, in: Evolutionary Computation, 2001, vol. 9, no 2, pp. 159-195.
[73]
F. Hutter, Y. Hamadi, H. H. Hoos, K. Leyton-Brown.
Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms, in: Principles and Practice of Constraint Programming (CP'06), 2006, pp. 213–228.