Nombre de documents

60

CV de Preux Philippe


Communication dans un congrès37 documents

  • Vincenzo Musco, Antonin Carette, Martin Monperrus, Philippe Preux. A Learning Algorithm for Change Impact Prediction. 5th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, May 2016, Austin, United States. <hal-01279620>
  • Vincenzo Musco, Martin Monperrus, Philippe Preux. Mutation-Based Graph Inference for Fault Localization. International Working Conference on Source Code Analysis and Manipulation, Oct 2016, Raleigh, United States. <hal-01350515>
  • Frédéric Guillou, Romaric Gaudel, Philippe Preux. Compromis exploration-exploitation pour système de recommandation à grande échelle. Conférence francophone sur l'Apprentissage Automatique (CAp'16), Jul 2016, Marseille, France. <hal-01406439>
  • Frédéric Guillou, Romaric Gaudel, Philippe Preux. Scalable explore-exploit Collaborative Filtering. Pacific Asia Conference on Information Systems (PACIS'16), 2016, Chiayi, Taiwan. 2016. <hal-01406418>
  • Frédéric Guillou, Romaric Gaudel, Philippe Preux. Sequential Collaborative Ranking Using (No-)Click Implicit Feedback. The 23rd International Conference on Neural Information Processing (ICONIP'16), Oct 2016, Kyoto, Japan. 9948, pp.288 - 296, 2016, Lecture Notes in Computer Science. <10.1007/978-3-319-46672-9_33>. <hal-01406338>
  • Crícia Felício, Klérisson Paixão, Celia Barcelos, Philippe Preux. Preference-like Score to Cope with Cold-Start User in Recommender Systems. 28th International Conference on Tools with Artificial Intelligence (ICTAI), Nov 2016, San Jose, United States. 2016, Proceedings of the IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). <hal-01390762>
  • Jérémie Mary, Romaric Gaudel, Philippe Preux. Bandits and Recommender Systems. First International Workshop on Machine Learning, Optimization, and Big Data (MOD'15), Jul 2015, Taormina, Italy. Springer International Publishing, Lecture Notes in Computer Science, 9432, pp.325-336, 2016, Lecture Notes in Computer Science. <http://link.springer.com/chapter/10.1007/978-3-319-27926-8_29>. <10.1007/978-3-319-27926-8_29>. <hal-01256033>
  • Bilel Derbel, Philippe Preux. Simultaneous Optimistic Optimization on the Noiseless BBOB Testbed. The 17th IEEE Congress on Evolutionary Computation (CEC), May 2015, Sendai, Japan. 2015. <hal-01246420>
  • Frédéric Guillou, Romaric Gaudel, Philippe Preux. Collaborative Filtering as a Multi-Armed Bandit. NIPS'15 Workshop: Machine Learning for eCommerce, Dec 2015, Montréal, Canada. <https://sites.google.com/site/nips15ecommerce/home>. <hal-01256254>
  • Vincenzo Musco, Martin Monperrus, Philippe Preux. An Experimental Protocol for Analyzing the Accuracy of Software Error Impact Analysis. Tenth IEEE/ACM International Workshop on Automation of Software Test, May 2015, Florence, Italy. <hal-01120913>
  • Philippe Preux, Rémi Munos, Michal Valko. Bandits attack function optimization. IEEE Congress on Evolutionary Computation, Jul 2014, Beijing, China. <hal-00978637>
  • Olivier Nicol, Jérémie Mary, Philippe Preux. Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques. Eric Xing; Tony Jebara. International Conference on Machine Learning, Jun 2014, Beijing, China. 32, 2014, Journal of Machine Learning Research, Workshop and Conference Proceedings; Proceedings of The 31st International Conference on Machine Learning. <http://jmlr.org/proceedings/papers/v32/>. <hal-00990840>
  • Hachem Kadri, Mohammad Ghavamzadeh, Philippe Preux. A Generalized Kernel Approach to Structured Output Learning. International Conference on Machine Learning (ICML), Jun 2013, Atlanta, United States. 2013. <hal-00695631v2>
  • Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari. Classification Localement Parcimonieuse par Méthodes Séquentielles. CAP 2012 - Conférence Francophone sur l'Apprentissage Automatique, May 2012, Nancy, France. 2012. <hal-01357567>
  • Azadeh Khaleghi, Daniil Ryabko, Jérémie Mary, Philippe Preux. Online Clustering of Processes. AISTATS 2012, 2012, La Palma, Spain. 22, pp.601-609, 2012, JMLR W\&CP. <hal-00765462>
  • Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari. Apprentissage par renforcement rapide pour des grands ensembles d'actions en utilisant des codes correcteurs d'erreur. Olivier Buffet. Journées Francophones sur la planification, la décision et l'apprentissage pour le contrôle des systèmes - JFPDA 2012, May 2012, Villers-lès-Nancy, France. 12 p, 2012. <hal-00736322>
  • Hachem Kadri, Alain Rakotomamonjy, Francis Bach, Philippe Preux. Multiple Operator-valued Kernel Learning. Neural Information Processing Systems (NIPS), Dec 2012, Lake Tahoe, United States. 2012. <hal-00677012v2>
  • Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari. Fast Reinforcement Learning with Large Action Sets Using Error-Correcting Output Codes for MDP Factorization. European Conference on Machine Learning, Sep 2012, Bristol, United Kingdom. Springer, Machine Learning and Knowledge Discovery in Databases, 7524, pp.180-194, 2012, Lecture Notes in Computer Science. <http://link.springer.com/chapter/10.1007/978-3-642-33486-3_12>. <10.1007/978-3-642-33486-3_12>. <hal-00747729>
  • Hachem Kadri, Emmanuel Duflos, Philippe Preux. Learning vocal tract variables with multi-task kernels. 36th International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2011, Prague, Czech Republic. 2011. <hal-00826050>
  • Hachem Kadri, Asma Rabaoui, Philippe Preux, Emmanuel Duflos, Alain Rakotomamonjy. Functional Regularized Least Squares Classi cation with Operator-valued Kernels. Lise Getoor, Tobias Scheffer. 28th International Conference on Machine Learning (ICML), Jun 2011, Seattle, United States. ACM, pp.993--1000, 2011. <hal-00772406>
  • Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari. Datum-wise classification. A sequential Approach to sparsity. ECML PKDD 2011 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2011, Athens, Greece. Springer, 6911, pp.375-390, 2011, Lecture Notes in Computer Science. <10.1007/978-3-642-23780-5_34>. <hal-00772986>
  • Hachem Kadri, Philippe Preux, Emmanuel Duflos, Stéphane Canu. Multiple functional regression with both discrete and continuous covariates. Fréderic Ferraty. 2nd International Workshop on Functional and Operatorial Statistics (IWFOS), Jun 2011, Santander, Spain. Physica-Verlag/Springer, pp.189-195, 2011, Contributions to Statistics. <hal-00772425>
  • Olivier Nicol, Jérémie Mary, Philippe Preux. ICML Exploration & Exploitation challenge: Keep it simple!. Dorota Glowacka and Louis Dorard and John Shawe-Taylor. Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2, Jul 2011, Bellevue, Washington, United States. sans, 26, pp.62-85, 2012, Journal of Machine Learning Research - Proceedings Track. <hal-00747725>
  • Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stephane Canu, Manuel Davy. Nonlinear functional regression: a functional RKHS approach. Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS'10), 2010, Italy. 9, pp.374-380, 2010. <hal-00510411>
  • Sertan Girgin, Jérémie Mary, Philippe Preux, Olivier Nicol. Advertising Campaigns Management: Should We Be Greedy?. IEEE International Conference on Data Mining, Dec 2010, Sydney, Australia. IEEE, pp.821-826, 2010. <hal-00772447>
  • Victor Gabillon, Jérémie Mary, Philippe Preux. Affichage de publicités sur des portails web. Extraction, Gestion des Connaissances (EGC), Jan 2010, Tunisie. pp.110-120, 2010. <hal-00772531>
  • Sertan Girgin, Jérémie Mary, Philippe Preux, Olivier Nicol. Planning-based Approach for Optimizing the Display of Online Advertising Campaigns. NIPS workshop on Machine Learning in Online ADvertising, Dec 2010, Whistler, Canada. <hal-00772512>
  • Manuel Loth, Philippe Preux. The Iso-regularization Descent Algorithm for the LASSO. 17th International Conference on Neural Information Processing, Nov 2010, Sidney, Australia. 2010. <inria-00508257v2>
  • Loth Manuel, Preux Philippe, Delepoulle Samuel, Renaud Christophe. ECON: a Kernel Basis Pursuit Algorithm with Automatic Feature Parameter Tuning, and its Application to Photometric Solids Approximation. IEEE Press. International Conference on Machine Learning and Applications, Dec 2009, Miami, United States. 2009. <inria-00430578>
  • Philippe Preux, Sertan Girgin, Manuel Loth. Feature Discovery in Approximate Dynamic Programming. Approximate Dynamic Programming and Reinforcement Learning, Mar 2009, Nashville, United States. IEEE, 2009. <hal-00351144>
  • Sertan Girgin, Philippe Preux. Incremental Basis Function Expansion in Reinforcement Learning using Cascade-Correlation Networks. 8th International Conference on Machine Learning and Applications, Dec 2008, San Diego, United States. IEEE Press, pp.75-82, 2008, Proc. of the International Conference on Machine Learning and Applications (ICML-A). <inria-00356262>
  • Sertan Girgin, Philippe Preux. Basis Expansion in Natural Actor Critic Methods. Girgin, Loth, Munos, Preux. European Workshop on Reinforcement Learning, Jun 2008, Villeneuve d'Ascq, France. Springer, 5323, pp.110-123, 2008, LNAI; Recent Advances in Reinforcement Learning. <hal-00826055>
  • Sertan Girgin, Philippe Preux. Feature discovery in reinforcement learning using genetic programming. 11th European Conference on Genetic Programming (EUROGP), 2008, Naples, Italy. Springer, 4971, pp.218-229, 2008, LNCS. <http://link.springer.com/chapter/10.1007%2F978-3-540-78671-9_19>. <hal-00826056>
  • Sertan Girgin, Philippe Preux. Basis Function Construction in Reinforcement Learning using Cascade-Correlation Learning Architecture. International Conference on Machine Learning and Applications, Dec 2008, San Diego, United States. IEEE Press, pp.75-82, 2008, Proceedings of the International Conference on Machine Learning and Applications (ICML-A). <hal-00826054>
  • Manuel Loth, Philippe Preux, Manuel Davy. A Unified View of TD Algorithms; Introducing Full-Gradient TD and Equi-Gradient Descent TD. European Symposium on Artificial Neural Networks, Apr 2007, Bruges, Belgium, Belgium. 2007. <inria-00116936v2>
  • Manuel Loth, Manuel Davy, Philippe Preux. Sparse Temporal Difference Learning using LASSO. IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, Apr 2007, Hawaï, USA, United States. 2007. <inria-00117075>
  • Manuel Loth, Manuel Davy, Rémi Coulom, Philippe Preux. Equi-Gradient Temporal Difference Learning. Kernel Methods and Reinforcement Learning, workshop of ICML 2006, Jun 2006, Pittsburgh, USA, United States. 2006. <inria-00117178>

Rapport9 documents

  • Benjamin Danglot, Philippe Preux, Benoit Baudry, Martin Monperrus. Correctness Attraction: A Study of Stability of Software Behavior Under Runtime Perturbation. [Research Report] hal-01378523, HAL. 2016. <hal-01378523>
  • Vincenzo Musco, Martin Monperrus, Philippe Preux. A Generative Model of Software Dependency Graphs to Better Understand Software Evolution. [Technical Report] Inria. 2014. <hal-01078716>
  • Jérémie Mary, Romaric Gaudel, Philippe Preux. Bandits Warm-up Cold Recommender Systems. [Research Report] RR-8563, INRIA Lille; INRIA. 2014, pp.18. <hal-01022628>
  • Hachem Kadri, Philippe Preux, Emmanuel Duflos, Stephane Canu. Operator-Valued Kernels for Nonparametric Operator Estimation. [Research Report] RR-7607, INRIA. 2011. <inria-00587649>
  • Sertan Girgin, Jérémie Mary, Philippe Preux, Olivier Nicol. Advertising Campaigns Management: Should We Be Greedy?. [Research Report] RR-7388, INRIA. 2010, pp.27. <inria-00519694>
  • Hachem Kadri, Emmanuel Duflos, Manuel Davy, Philippe Preux, Stephane Canu. General Framework for Nonlinear Functional Regression with Reproducing Kernel Hilbert Spaces. [Research Report] RR-6908, INRIA. 2009. <inria-00378381>
  • Manuel Loth, Philippe Preux. The Equi-Correlation Network: a New Kernelized-LARS with Automatic Kernel Parameters Tuning. [Research Report] RR-6794, INRIA. 2008. <inria-00351930>
  • Sertan Girgin, Philippe Preux. Incremental Basis Function Expansion in Reinforcement Learning using Cascade-Correlation Networks. [Research Report] RR-6505, INRIA. 2008. <inria-00272368v2>
  • Sertan Girgin, Philippe Preux. Feature Discovery in Reinforcement Learning using Genetic Programming. [Research Report] INRIA. 2007. <inria-00187997v2>

Article dans une revue6 documents

  • Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Alain Rakotomamonjy, et al.. Operator-valued Kernels for Learning from Functional Response Data. Journal of Machine Learning Research (JMLR), 2016. <hal-01221329v2>
  • Crícia Felício, Klérisson Paixão, Guilherme Alves, Sandra De Amo, Philippe Preux. Exploiting Social Information in Pairwise Preference Recommender System. Journal of Information and Data Management, Brazilian Computer Society, 2016, 7 (2), pp.16. <hal-01462200>
  • Vincenzo Musco, Martin Monperrus, Philippe Preux. A Large-scale Study of Call Graph-based Impact Prediction using Mutation Testing. Software Quality Journal, Springer Verlag, 2016, <10.1007/s11219-016-9332-8>. <hal-01346046>
  • Azadeh Khaleghi, Daniil Ryabko, Jérémie Mary, Philippe Preux. Consistent Algorithms for Clustering Time Series. Journal of Machine Learning Research, Journal of Machine Learning Research, 2016, 17 (3), pp.1 - 32. <hal-01399613>
  • Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari. Sequential approaches for learning datum-wise sparse representations. Machine Learning, Springer Verlag, 2012, 89 (1-2), pp.87-122. <http://link.springer.com/article/10.1007%2Fs10994-012-5306-7>. <10.1007/s10994-012-5306-7>. <hal-00747724>
  • Sertan Girgin, Jérémie Mary, Philippe Preux, Olivier Nicol. Managing advertising campaigns -- an approximate planning approach. Frontiers of Computer Science -Springer-, Springer Verlag, 2012, 6 (2), pp.209-229. <http://rd.springer.com/article/10.1007/s11704-012-2873-5>. <10.1007/s11704-012-2873-5>. <hal-00747722>

Autre publication2 documents

  • Philippe Preux, Marc Tommasi, Thierry Vieville, Colin De La Higuera. L’apprentissage automatique : le diable n’est pas dans l’algorithme. Article sur http://binaire.blog.lemonde.fr. 2015. <hal-01246178>
  • Frédéric Guillou, Romaric Gaudel, Jérémie Mary, Philippe Preux. User Engagement as Evaluation: a Ranking or a Regression Problem?. 1. Introduction 2. Recsys Challenge 2014: Data and Protocol 2.1 Data Characteristics and St.. 2014, <10.1145/2668067.2668073>. <hal-01077986>

Pré-publication, Document de travail2 documents

  • Vincenzo Musco, Antonin Carette, Martin Monperrus, Philippe Preux. A Learning Algorithm for Change Impact Prediction: Experimentation on 7 Java Applications. 2015. <hal-01248241>
  • Gabriel Dulac-Arnold, Ludovic Denoyer, Philippe Preux, Patrick Gallinari. Datum-Wise Classification: A Sequential Approach to Sparsity. ECML2011. 2011. <hal-00617913>

Chapitre d'ouvrage3 documents

  • Frédéric Guillou, Romaric Gaudel, Philippe Preux. Large-scale Bandit Recommender System. Pardalos, Panos M.; Conca, Piero; Giuffrida, Giovanni; Nicosia, Giuseppe. Machine Learning, Optimization, and Big Data: Second International Workshop (MOD 2016), Volterra, Italy, August 26-29, 2016, Revised Selected Papers, 10122, Springer International Publishing, pp.11, 2016, Lecture Notes in Computer Science, 978-3-319-51469-7. <10.1007/978-3-319-51469-7_17>. <hal-01406389>
  • Delepoulle Samuel, François Rouselle, Renaud Christophe, Philippe Preux. A comparison of two machine learning approaches for Photometric Solids Compression. Plemenos, Dimitri; Miaoulis, Georgios. Intelligent Computer Graphics, 321, Springer, pp.145-164, 2010, Studies in Computational Intelligence. <hal-00826051>
  • Delepoulle Samuel, Renaud Christophe, Philippe Preux. Light Source Storage and Interpolation for Global Illumination: a neural solution. Dimitri Plemenos, Georgios Miaoulis. Intelligent Computer Graphics, 240, Springer, pp.87-104, 2009, Studies in Computational Intelligence. <hal-00826053>

Direction d'ouvrage, Proceedings1 document

  • Sertan Girgin, Manuel Loth, Rémi Munos, Philippe Preux, Daniil Ryabko. Recent Advances in Reinforcement Learning. Springer, Lectures Notes in Artificial Intelligence (LNAI), vol. 5323, pp.281, 2009. <hal-00351128>