Bibliography

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.
  http://hal.inria.fr/tel-00842447
• 2A. 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
• 3J.-B. Hoock.
  Contributions to Simulation-based High-dimensional Sequential Decision Making, Université Paris Sud - Paris XI, April 2013.
  http://hal.inria.fr/tel-00912338
• 4I. 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
• 5V. 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
• 6J.-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

• 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 ]
  http://hal.inria.fr/hal-00862563
• 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).
  http://hal.inria.fr/hal-00867370
• 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 ]
  http://hal.inria.fr/in2p3-00869803
• 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, n^o 6.
  http://hal.inria.fr/hal-00927587
• 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.
  http://hal.inria.fr/hal-00738384
• 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.
  http://hal.inria.fr/hal-00863577
• 13D. Feng, C. Germain-Renaud, T. Glatard.
  Efficient Distributed Monitoring with Active Collaborative Prediction, in: Future Generation Computer Systems, 2013, vol. 29, n^o 8, pp. 2272-2283. [ DOI : 10.1016/j.future.2013.06.001 ]
  http://hal.inria.fr/hal-00784038
• 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 ]
  http://hal.inria.fr/hal-00865085
• 15C. Furtlehner.
  Pairwise MRF Models Selection for Traffic Inference, in: Interdisciplinary Information Sciences, August 2013, vol. 19, n^o 1, pp. 17-22. [ DOI : 10.4036/iis.2013.17 ]
  http://hal.inria.fr/hal-00865089
• 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, n^o 2, pp. 326–334. [ DOI : 10.1016/j.mbs.2013.10.003 ]
  http://hal.inria.fr/hal-00909809
• 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, n^o 2, pp. 156-177. [ DOI : 10.1177/0037549712457852 ]
  http://hal.inria.fr/hal-00738218
• 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.
  http://hal.inria.fr/hal-00862455
• 19W. Wang, M. Sebag.
  Hypervolume indicator and dominance reward based multi-objective Monte-Carlo Tree Search, in: Machine Learning, May 2013, vol. 92, n^o 2-3, pp. 403-429. [ DOI : 10.1007/s10994-013-5369-0 ]
  http://hal.inria.fr/hal-00852048
• 20X. 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

• 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.
  http://hal.inria.fr/hal-00801414
• 22Y. 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
• 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 ]
  http://hal.inria.fr/hal-00863584
• 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.
  http://hal.inria.fr/hal-00862605
• 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.
  http://hal.inria.fr/hal-00862621
• 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.
  http://hal.inria.fr/hal-00825840
• 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.
  http://hal.inria.fr/hal-00835352
• 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 ]
  http://hal.inria.fr/in2p3-00846142
• 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.
  http://hal.inria.fr/in2p3-00907381
• 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.
  http://hal.inria.fr/hal-00862603
• 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.
  http://hal.inria.fr/hal-00862816
• 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.
  http://hal.inria.fr/hal-00907671
• 33J. 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
• 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.
  http://hal.inria.fr/hal-00834494
• 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.
  http://hal.inria.fr/hal-00834498
• 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.
  http://hal.inria.fr/hal-00924062
• 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.
  http://hal.inria.fr/hal-00862645
• 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.
  http://hal.inria.fr/hal-00820634
• 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.
  http://hal.inria.fr/hal-00750560
• 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.
  http://hal.inria.fr/hal-00835003
• 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.
  http://hal.inria.fr/hal-00820617
• 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 ]
  http://hal.inria.fr/in2p3-00846125
• 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.
  http://hal.inria.fr/hal-00818596
• 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.
  http://hal.inria.fr/hal-00818595
• 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.
  http://hal.inria.fr/hal-00851189
• 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.
  http://hal.inria.fr/hal-00863451
• 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.
  http://hal.inria.fr/hal-00863453
• 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.
  http://hal.inria.fr/hal-00862243
• 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.
  http://hal.inria.fr/hal-00862223
• 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.
  http://hal.inria.fr/hal-00862186
• 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 ]
  http://hal.inria.fr/hal-00832184
• 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.
  http://hal.inria.fr/in2p3-00907406

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.
  http://hal.inria.fr/hal-00931347
• 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.
  http://hal.inria.fr/hal-00862707
• 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.
  http://hal.inria.fr/hal-00844490

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.
  http://hal.inria.fr/hal-00808450
• 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.
  http://hal.inria.fr/hal-00858008

Internal Reports

• 58V. Martin, J.-M. Lasgouttes, C. Furtlehner.
  Using Latent Binary Variables for Online Reconstruction of Large Scale Systems, Inria, December 2013, n^o RR-8435, 34 p.
  http://hal.inria.fr/hal-00922106
• 59M. Misir, M. Sebag.
  Algorithm Selection as a Collaborative Filtering Problem, December 2013, 43 p.
  http://hal.inria.fr/hal-00922840

Other Publications

• 60L. Arnold, Y. Ollivier.
  Layer-wise learning of deep generative models, 2013.
  http://hal.inria.fr/hal-00794302
• 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.
  http://hal.inria.fr/hal-00844305
• 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.
  http://hal.inria.fr/hal-00877161
• 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.
  http://hal.inria.fr/hal-00877160
• 64N. 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
• 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.
  http://hal.inria.fr/hal-00857980
• 66Y. Ollivier.
  Riemannian metrics for neural networks, 2013, (3rd version: tensor square derivative metric discussed).
  http://hal.inria.fr/hal-00857982

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.
  http://hal.inria.fr/in2p3-00698455
• 69H.-G. Beyer.
  Evolution Strategies, in: Scholarpedia, 2007, vol. 2, n^o 8, 1965 p. [ DOI : 10.4249/scholarpedia.1965 ]
  http://www.scholarpedia.org/article/Evolution_strategies
• 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.
  http://hal.archives-ouvertes.fr/docs/00/45/62/92/PDF/icaps10.pdf
• 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, n^o 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, n^o 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.