Publications of the year

Doctoral Dissertations and Habilitation Theses

  • 1L. Arnold.

    Learning Deep Representations : Toward a better new understanding of the deep learning paradigm, Université Paris Sud - Paris XI, June 2013.

  • 2A. Couetoux.

    Monte Carlo Tree Search pour les problèmes de décision séquentielle en milieu continus et stochastiques, Université Paris Sud - Paris XI, September 2013.

  • 3J.-B. Hoock.

    Contributions to Simulation-based High-dimensional Sequential Decision Making, Université Paris Sud - Paris XI, April 2013.

  • 4I. Loshchilov.

    Surrogate-Assisted Evolutionary Algorithms, Université Paris Sud - Paris XI and Institut national de recherche en informatique et en automatique - Inria, January 2013.

  • 5V. Martin.

    Modélisation probabiliste et inférence par l'algorithme Belief Propagation, Ecole Nationale Supérieure des Mines de Paris, May 2013.

  • 6J.-m. Montanier.

    Environment-driven Distributed Evolutionary Adaptation for Collective Robotic Systems, Université Paris Sud - Paris XI, March 2013.


Articles in International Peer-Reviewed Journals

  • 7J. 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 ]

  • 8N. 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).

  • 9R. 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 ]

  • 10P. 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.

  • 11P. Caillou, J. Gil-Quijano, X. Zhou.

    Automated observation of multi-agent based simulations: a statistical analysis approach, in: Studia Informatica Universalis, 2013, to appear.

  • 12C.-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.

  • 13D. 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 ]

  • 14C. 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 ]

  • 15C. 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 ]

  • 16G. 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 ]

  • 17A. 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 ]

  • 18O. 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.

  • 19W. 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 ]

  • 20X. Zhang, C. Furtlehner, C. Germain-Renaud, M. Sebag.

    Data Stream Clustering with Affinity Propagation, in: IEEE Transactions on Knowledge and Data Engineering, 2014.


International Conferences with Proceedings

  • 21O. 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.

  • 22Y. Akimoto, Y. Ollivier.

    Objective Improvement in Information-Geometric Optimization, in: Foundations of Genetic Algorithms XII, Adelaide, Australia, January 2013.

  • 23S. 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 ]

  • 24J. 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.

  • 25J. 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.

  • 26A. 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.

  • 27D. 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.

  • 28R. 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 ]

  • 29R. 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.

  • 30I. 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.

  • 31M. 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.

  • 32J. 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.

  • 33J. Decock, O. Teytaud.

    Noisy Optimization Complexity Under Locality Assumption, in: FOGA - Foundations of Genetic Algorithms XII - 2013, Adelaide, Australia, February 2013.

  • 34A. 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.

  • 35A. 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.

  • 36N. 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.

  • 37Y. 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.

  • 38M. 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.

  • 39M. 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.

  • 40M. 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.

  • 41M. 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.

  • 42B. 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 ]

  • 43I. 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.

  • 44I. 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.

  • 45I. 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.

  • 46M. 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.

  • 47M. 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.

  • 48G. 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.

  • 49G. 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.

  • 50G. 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.

  • 51B. 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 ]

  • 52B. 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.


Conferences without Proceedings

  • 53R. 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.

  • 54Y. 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.

  • 55N. 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.


Scientific Books (or Scientific Book chapters)

  • 56N. 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.

  • 57Y. 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.


Internal Reports

  • 58V. 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.

  • 59M. Misir, M. Sebag.

    Algorithm Selection as a Collaborative Filtering Problem, December 2013, 43 p.


Other Publications

  • 60L. Arnold, Y. Ollivier.

    Layer-wise learning of deep generative models, 2013.

  • 61S. 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.

  • 62A. 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.

  • 63A. 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.

  • 64N. Lim, F. D'Alché-Buc, C. Auliac, G. Michailidis.

    Operator-valued Kernel-based Vector Autoregressive Models for Network Inference, October 2013.

  • 65Y. 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.

  • 66Y. Ollivier.

    Riemannian metrics for neural networks, 2013, (3rd version: tensor square derivative metric discussed).

References in notes
  • 67R. 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.
  • 68D. 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.

  • 69H.-G. Beyer.

    Evolution Strategies, in: Scholarpedia, 2007, vol. 2, no 8, 1965 p. [ DOI : 10.4249/scholarpedia.1965 ]

  • 70J. 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.

  • 71G. 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.
  • 72N. Hansen, A. Ostermeier.

    Completely Derandomized Self-Adaptation in Evolution Strategies, in: Evolutionary Computation, 2001, vol. 9, no 2, pp. 159-195.
  • 73F. 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.