Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

• 1R. Akrour.
  Robust Preference Learning-based Reinforcement Learning, Université Paris-Sud, September 2014.
  https://hal.inria.fr/tel-01111276
• 2J. Decock.
  Hybridization of dynamic optimization methodologies, Université Paris Sud, November 2014.
  https://hal.inria.fr/tel-01103935
• 3D. Feng.
  Efficient end-to-end monitoring for fault management in distributed systems, Université Paris Sud - Paris XI, March 2014.
  https://tel.archives-ouvertes.fr/tel-01017083
• 4G. Marceau Caron.
  Optimization and Uncertainty Handling in Air Traffic Management, Paris-Sud XI, September 2014.
  https://hal.inria.fr/tel-01080370
• 5W. Wang.
  Multi-objective sequential decision making, Université Paris Sud - Paris XI, July 2014.
  https://tel.archives-ouvertes.fr/tel-01057079

Articles in International Peer-Reviewed Journals

• 6N. J.-B. Brunel, Q. Clairon, F. D'Alché-Buc.
  Parametric Estimation of Ordinary Differential Equations with Orthogonality Conditions, in: Journal of American Statistics Association, 2014, vol. 109, n^o 505, pp. 173–185. [ DOI : 10.1080/01621459.2013.841583 ]
  https://hal.archives-ouvertes.fr/hal-00867370
• 7P. 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.
  https://hal.inria.fr/hal-00927587
• 8C.-S. Lee, M.-H. Wang, M.-J. Wu, O. Teytaud, S.-J. Yen.
  T2FS-based Adaptive Linguistic Assessment System for Semantic Analysis and Human Performance Evaluation on Game of Go, in: IEEE Transactions on Fuzzy Systems, 2014, 22 p.
  https://hal.inria.fr/hal-01059822
• 9M. Sebag.
  A tour of Machine Learning: an AI perspective, in: AI Communications, 2014, vol. 27, n^o 1, pp. 11-23. [ DOI : 10.3233/AIC-130580 ]
  https://hal.inria.fr/hal-01109768
• 10Y. Shogo, M. Ohzeki, A. Decelle.
  Detection of Cheating by Decimation Algorithm, in: Journal of the Physical Society of Japan, January 2015, vol. 84, 024801. [ DOI : 10.7566/JPSJ.84.024801 ]
  https://hal.archives-ouvertes.fr/hal-01105415
• 11K. Shun, Y. Muneki, C. Furtlehner, K. Tanaka.
  Traffic data reconstruction based on Markov random field modeling , in: Inverse Problems, 2014, vol. 30, n^o 2, 15 p.
  https://hal.inria.fr/hal-01096947
• 12X. Zhang, C. Furtlehner, C. Germain-Renaud, M. Sebag.
  Data Stream Clustering with Affinity Propagation, in: IEEE Transactions on Knowledge and Data Engineering, 2014.
  https://hal.inria.fr/hal-00862941

International Conferences with Proceedings

• 13C. Adam-Bourdarios, G. Cowan, C. Germain, I. Guyon, B. Kegl, D. Rousseau.
  The ATLAS Higgs Boson Machine Learning Challenge, in: International Conference on High Energy Physics(ICHEP) Conference, Valencia, Spain, July 2014, forthcoming.
  https://hal.inria.fr/hal-01111177
• 14Y. Akimoto, A. Auger, N. Hansen.
  Comparison-Based Natural Gradient Optimization in High Dimension, in: Genetic and Evolutionary Computation Conference GECCO'14, Vancouver, Canada, ACM, July 2014.
  https://hal.inria.fr/hal-00997835
• 15R. Akrour, M. Schoenauer, M. Sebag, J.-C. Souplet.
  Programming by Feedback, in: International Conference on Machine Learning, Pékin, China, June 2014.
  https://hal.inria.fr/hal-00980839
• 16S. Astete-Morales, M.-L. Cauwet, O. Teytaud.
  Evolution Strategies with Additive Noise: A Convergence Rate Lower Bound, in: Foundations of Genetic Algorithms, Aberythswyth, United Kingdom, 2015, 9 p.
  https://hal.inria.fr/hal-01077625
• 17D. Auger, J. Liu, S. Ruette, D. L. Saint-Pierre, O. Teytaud.
  Sparse Binary Zero-Sum Games, in: Asian Conference on Machine Learning, Ho-Chi-Minh-Ville, Vietnam, 2014, vol. 29, 16 p.
  https://hal.inria.fr/hal-01077627
• 18A. Bureau, M. Sebag.
  Bellmanian Bandit Network, in: Autonomously Learning Robots, at NIPS, Montréal, Canada, Gerhard Neumann (TU-Darmstadt) and Joelle Pineau (McGill University) and Peter Auer (Uni Leoben) and Marc Toussaint (Uni Stuttgart), December 2014.
  https://hal.inria.fr/hal-01102970
• 19M.-L. Cauwet.
  Noisy Optimization: Convergence with a Fixed Number of Resamplings, in: EvoStar, Granada, Spain, April 2014.
  https://hal.inria.fr/hal-00976063
• 20M.-L. Cauwet, J. Liu, O. Teytaud.
  Algorithm Portfolios for Noisy Optimization: Compare Solvers Early, in: Learning and Intelligent Optimization Conference, Florida, United States, February 2014.
  https://hal.inria.fr/hal-00926638
• 21M.-L. Cauwet, O. Teytaud, S.-Y. Chiu, K.-M. Lin, S.-J. Yen, D. L. Saint-Pierre, F. Teytaud.
  Parallel Evolutionary Algorithms Performing Pairwise Comparisons, in: Foundations of Genetic Algorithms, Aberythswyth, United Kingdom, 2015, 15 p.
  https://hal.inria.fr/hal-01077626
• 22A. Chotard, A. Auger, N. Hansen.
  Markov Chain Analysis of Evolution Strategies on a Linear Constraint Optimization Problem, in: IEEE Congress on Evolutionary Computation, Beijing, China, A. Hussain, Z. Zeng, N. Zhang (editors), http://www.ieee-wcci2014.org/committees.htm, July 2014.
  https://hal.inria.fr/hal-00977379
• 23A. Chotard, M. Holeňa.
  A Generalized Markov-Chain Modelling Approach to (1,λ)-ES Linear Optimization, in: 13th International Conference on Parallel Problem Solving from Nature, Ljubljana, Slovenia, Lecture Notes in Computer Science, Springer, September 2014, vol. 8672, pp. 902 - 911. [ DOI : 10.1007/978-3-319-10762-2_89 ]
  https://hal.inria.fr/hal-01091494
• 24J.-J. Christophe, J. Decock, O. Teytaud.
  Direct model predictive control, in: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, April 2014.
  https://hal.inria.fr/hal-00958192
• 25Y. Coadou, B. Kégl.
  Boosted Decision Tree and application, in: IN2P3 School of Statistics 2014, Autrans, France, May 2014.
  https://hal.inria.fr/in2p3-00997259
• 26D. Feng, C. Germain-Renaud, J. Nauroy.
  Sequential fault monitoring, in: Cloud and Autonomic Computing, London, United Kingdom, IEEE, September 2014.
  https://hal.inria.fr/hal-01064161
• 27C. Germain, J. Nauroy, K. Rafes.
  The Grid Observatory 3.0 - Towards reproducible research and open collaborations using semantic technologies , in: EGI Community Forum 2014, Helsinki, Finland, May 2015.
  https://hal.inria.fr/hal-01104235
• 28B. Hanczar, M. Sebag.
  Combination of One-Class Support Vector Machines for Classification with Reject Option, in: Machine Learning and Knowledge Discovery in Databases - Part I, Nancy, France, T. Calders, F. Esposito, E. Hüllermeier, R. Meo (editors), Machine Learning and Knowledge Discovery in Databases - Part I, September 2014, vol. 8724, pp. 547 - 562. [ DOI : 10.1007/978-3-662-44848-9_35 ]
  https://hal.inria.fr/hal-01109774
• 29N. Hansen, A. Atamna, A. Auger.
  How to Assess Step-Size Adaptation Mechanisms in Randomised Search, in: 13th International Conference on Parallel Problem Solving from Nature, Ljubljana, Slovenia, T. Bartz-Beielstein (editor), LNCS, Springer, September 2014, vol. 8672, pp. 60-69.
  https://hal.inria.fr/hal-00997294
• 30B. Kégl.
  Center for data science, in: Paris-Saclay Center for Data Science Kick-off Meeting, Orsay, France, June 2014.
  https://hal.inria.fr/in2p3-01020019
• 31B. Kégl.
  Real time multivariate classifiers, in: Trigger, Online and Offline Computing Workshop, Geneve, Switzerland, September 2014.
  http://hal.in2p3.fr/in2p3-01070943
• 32B. Kégl, D. Rousseau, C. Germain, I. Guyon, G. Cowan.
  Introduction to the HEPML Workshop and the HiggsML challenge, in: HEPML workshop at NIPS14 - Neural Information Processing Systems Conference, Montreal, Canada, December 2014.
  http://hal.in2p3.fr/in2p3-01100982
• 33J. Liu, D. L. Saint-Pierre, O. Teytaud.
  A mathematically derived number of resamplings for noisy optimization, in: Companion - Genetic and Evolutionary Computation Conference (GECCO 2014), Vancouver, Canada, ACM, July 2014, pp. 61-62.
  https://hal.inria.fr/hal-00979442
• 34J. Liu, O. Teytaud.
  Meta online learning: experiments on a unit commitment problem, in: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, April 2014.
  https://hal.inria.fr/hal-00973397
• 35A. Llamosi, A. Mezine, F. D'Alché-Buc, V. Letort, M. Sebag.
  Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach, in: Machine Learning and Knowledge Discovery in Databases - Part II, Nancy, France, Springer Verlag, September 2014, vol. 8725, pp. 306 - 321. [ DOI : 10.1007/978-3-662-44851-9_20 ]
  https://hal.inria.fr/hal-01109775
• 37L. Malagò, G. Pistone.
  Information Geometry of Gaussian Distributions in View of Stochastic Optimization, in: Foundations of Genetic Algorithms XIII, Aberystwyth, United Kingdom, Jun He, Thomas Jansen, Gabriela Ochoa and Christine Zarges, January 2015.
  https://hal.inria.fr/hal-01108986
• 38G. Marceau, M. Schoenauer.
  Racing Multi-Objective Selection Probabilities, in: 13th International Conference on Parallel Problem Solving from Nature, Ljubljana, Slovenia, September 2014, 1 p.
  https://hal.archives-ouvertes.fr/hal-01009907
• 39V. Martin, C. Furtlehner, Y. Han, J.-M. Lasgouttes.
  GMRF Estimation under Topological and Spectral Constraints, in: 7th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Nancy, France, T. Calders, F. Esposito, E. Hüllermeier, R. Meo (editors), Lecture Notes in Computer Science, Springer Berlin Heidelberg, September 2014, vol. 8725, pp. 370-385. [ DOI : 10.1007/978-3-662-44851-9_24 ]
  https://hal.archives-ouvertes.fr/hal-01065607
• 40A. Quemy, M. Schoenauer.
  True Pareto Fronts for Multi-Objective AI Planning Instances, in: European Conference on Combinatorial Optimization - EvoCOP, Copenhague, Denmark, F. Chicano, G. Ochoa (editors), LNCS, Springer Verlag, April 2015, forthcoming.
  https://hal.archives-ouvertes.fr/hal-01109777
• 41A. Quemy, M. Schoenauer, V. Vidal, J. Dréo, P. Savéant.
  Solving Large MultiZenoTravel Benchmarks with Divide-and-Evolve, in: Learning and Intelligent OptimizatioN - LION 9, Lille, France, C. Dhaenens, L. Jourdan, M.-E. Marmion (editors), LNCS, Springer Verlag, January 2015, 7 p.
  https://hal.archives-ouvertes.fr/hal-01109776
• 42K. Rafes, J. Nauroy, C. Germain.
  TFT, Tests For Triplestores , in: Semantic Web Challenge, part of the International Semantic Web Conference, Riva Del Garda, Italy, October 2014.
  https://hal.inria.fr/hal-01104252
• 43J. Ribeiro, J. Carmona, M. Mısır, M. Sebag.
  A Recommender System for Process Discovery, in: Business Process Management, Eindhoven, Netherlands, S. Sadiq, P. Soffer, H. Völzer (editors), Proc. 12th Business Process Management, Springer Verlag, September 2014, vol. 8659, pp. 67 - 83. [ DOI : 10.1007/978-3-319-10172-9_5 ]
  https://hal.inria.fr/hal-01109766
• 44D. L. Saint-Pierre, O. Teytaud.
  Nash and the Bandit Approach for Adversarial Portfolios, in: CIG 2014 - Computational Intelligence in Games, Dortmund, Germany, Computational Intelligence in Games, IEEE, August 2014, 7 p. [ DOI : 10.1109/CIG.2014.6932897 ]
  https://hal.inria.fr/hal-01077628
• 45D. L. St-Pierre, J. Liu.
  Differential Evolution Algorithm Applied to Non-Stationary Bandit Problem, in: 2014 IEEE Congress on Evolutionary Computation (IEEE CEC 2014), Beijing, China, July 2014.
  https://hal.inria.fr/hal-00979456
• 46G. Zhang, M. Sebag.
  Coupling Evolution and Information Theory for Autonomous Robotic Exploration, in: 13th International Conference on Parallel Problem Solving from Nature, Ljubliana, Slovenia, T. Bartz-Beielstein, J. Branke, B. Filipic, J. Smith (editors), Lecture Notes in Computer Science, Springer Verlag, September 2014, vol. 8672, pp. 852 - 861. [ DOI : 10.1007/978-3-319-10762-2_84 ]
  https://hal.inria.fr/hal-01109770

National Conferences with Proceedings

• 47B. Kégl.
  Introduction to MVA approach, in: IN2P3 School of Statistics 2014, Autrans, France, May 2014.
  https://hal.inria.fr/in2p3-00997253

Conferences without Proceedings

• 48B. Kégl.
  La science des données pour les données de la science, in: 9th Journées Informatique de l'IN2P3-IRFU, Le Grau du Roi, France, October 2014.
  http://hal.in2p3.fr/in2p3-01076155

Scientific Books (or Scientific Book chapters)

• 49CMA-ES: A Function Value Free Second Order Optimization Method, 2014.
  https://hal.inria.fr/hal-01110313
• 50N. 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, 2014, pp. 145-180.
  https://hal.inria.fr/hal-00808450
• 51S. Rebecchi, H. Paugam-Moisy, M. Sebag.
  Learning Sparse Features with an Auto-Associator, in: Growing Adaptive Machines, T. Kowaliw, N. Bredeche, R. Doursat (editors), Studies in Computational Intelligence, Springer Verlag, 2014, vol. 557, pp. 139 - 158. [ DOI : 10.1007/978-3-642-55337-0_4 ]
  https://hal.inria.fr/hal-01109773

Books or Proceedings Editing

• 52Y. Ollivier, H. Pajot, C. Villani (editors)
  Optimal Transportation , London Mathematical Society Lecture Note Series, Cambridge University Press, Grenoble, France, 2014, vol. 413.
  https://hal.archives-ouvertes.fr/hal-01104763

Internal Reports

• 53A. Chotard, M. Holena.
  A Generalized Markov-Chain Modelling Approach to $(1,\lambda )$-ES Linear Optimization: Technical Report, June 2014.
  https://hal.inria.fr/hal-01003015

Scientific Popularization

• 54J. Decock, J.-J. Christophe, O. Teytaud.
  Optimization of Energy Policies Using Direct Value Search, May 2014, 9èmes Journées Francophones de Planification, Décision et Apprentissage (JFPDA'14).
  https://hal.inria.fr/hal-00997562

Other Publications

• 55C. Adam-Bourdarios, G. Cowan, C. Germain, I. Guyon, B. Kégl, D. Rousseau.
  Learning to discover: the Higgs boson machine learning challenge, May 2014. [ DOI : 10.7483/OPENDATA.ATLAS.MQ5J.GHXA ]
  https://hal.inria.fr/hal-01104487
• 56O. Ait Elhara, A. Auger, N. Hansen.
  Large-Scale Optimization of Low Effective and Low Epsilon-Effective Dimension Problems, February 2015.
  https://hal.inria.fr/hal-01112850
• 57A. Auger, N. Hansen.
  Linear Convergence of Comparison-based Step-size Adaptive Randomized Search via Stability of Markov Chains, May 2014.
  https://hal.inria.fr/hal-00877160
• 58N. Lim, F. D'Alché-Buc, C. Auliac, G. Michailidis.
  Operator-valued Kernel-based Vector Autoregressive Models for Network Inference, March 2014.
  https://hal.archives-ouvertes.fr/hal-00872342
• 59G. Marceau, M. Schoenauer.
  Racing Multi-Objective Selection Probabilities, June 2014, Extended pre-print of PPSN 2014 paper.
  https://hal.inria.fr/hal-01002854
• 60B. Mayeur, R. Akrour, M. Sebag.
  Direct Value Learning: a Rank-Invariant Approach to Reinforcement Learning, October 2014.
  https://hal.archives-ouvertes.fr/hal-01090982
• 61U.-M. O'Reilly, A. Esparcia-Alcazar, A. Auger, C. Doerr, A. Ekart, G. Ochoa.
  Women@GECCO 2014, 2014, 2 p, Summary of the Women@GECCO meeting. [ DOI : 10.1145/2598394.2611386 ]
  http://hal.upmc.fr/hal-01086538
• 62Y. Ollivier.
  Auto-encoders: reconstruction versus compression, 2014.
  https://hal.archives-ouvertes.fr/hal-01104268

References in notes

• 63D. 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
• 64H.-G. Beyer.
  Evolution Strategies, in: Scholarpedia, 2007, vol. 2, n^o 8, 1965. [ DOI : 10.4249/scholarpedia.1965 ]
  http://www.scholarpedia.org/article/Evolution_strategies
• 65N. Hansen, A. Ostermeier.
  Completely Derandomized Self-Adaptation in Evolution Strategies, in: Evolutionary Computation, 2001, vol. 9, n^o 2, pp. 159-195.