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Improving offline evaluation of contextual bandit algorithms via bootstrapping techniques

 Olivier Nicol , Jérémie Mary , Philippe Preux
International Conference on Machine Learning, Jun 2014, Beijing, China
Communication dans un congrès hal-00990840v1

Compromis exploration-exploitation pour système de recommandation à grande échelle

 Frédéric Guillou , Romaric Gaudel , Philippe Preux
Conférence francophone sur l'Apprentissage Automatique (CAp'16), Jul 2016, Marseille, France
Communication dans un congrès hal-01406439v1
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Large-scale Bandit Recommender System

 Frédéric Guillou , Romaric Gaudel , Philippe Preux
Proc. of the Second International Workshop on Machine Learning, Optimization and Big Data (MOD), Sep 2016, Volterra, Italy. pp.11, ⟨10.1007/978-3-319-51469-7_17⟩
Communication dans un congrès hal-01406389v1
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gym-DSSAT: a crop model turned into a Reinforcement Learning environment

 Romain Gautron , Emilio J. Padrón , Philippe Preux , Julien Bigot , Odalric-Ambrym Maillard , et al.
[Research Report] RR-9460, Inria Lille. 2022, pp.31
Rapport hal-03711132v4
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User Engagement as Evaluation: a Ranking or a Regression Problem?

 Frédéric Guillou , Romaric Gaudel , Jérémie Mary , Philippe Preux
2014, ⟨10.1145/2668067.2668073⟩
Autre publication scientifique hal-01077986v1
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Sparse Temporal Difference Learning using LASSO

 Manuel Loth , Manuel Davy , Philippe Preux
IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, Apr 2007, Hawaï, USA, United States
Communication dans un congrès inria-00117075v1
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The challenge of controlling microgrids in the presence of rare events with Deep Reinforcement Learning

 Tanguy Levent , Philippe Preux , Gonzague Henri , Réda Alami , Philippe Cordier , et al.
IET Smart Grid, In press, ⟨10.1049/stg2.12003⟩
Article dans une revue hal-02971554v1
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Consistent Algorithms for Clustering Time Series

 Azadeh Khaleghi , Daniil Ryabko , Jérémie Mary , Philippe Preux
Journal of Machine Learning Research, 2016, 17 (3), pp.1 - 32
Article dans une revue hal-01399613v1

Scalable explore-exploit Collaborative Filtering

 Frédéric Guillou , Romaric Gaudel , Philippe Preux
Pacific Asia Conference on Information Systems (PACIS'16), 2016, Chiayi, Taiwan
Communication dans un congrès hal-01406418v1
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Collaborative Filtering as a Multi-Armed Bandit

 Frédéric Guillou , Romaric Gaudel , Philippe Preux
NIPS'15 Workshop: Machine Learning for eCommerce, Dec 2015, Montréal, Canada
Communication dans un congrès hal-01256254v1
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Advertising Campaigns Management: Should We Be Greedy?

 Sertan Girgin , Jérémie Mary , Philippe Preux , Olivier Nicol
[Research Report] RR-7388, INRIA. 2010, pp.27
Rapport inria-00519694v1
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Equi-Gradient Temporal Difference Learning

 Manuel Loth , Manuel Davy , Rémi Coulom , Philippe Preux
Kernel Methods and Reinforcement Learning, workshop of ICML 2006, Jun 2006, Pittsburgh, USA, United States
Communication dans un congrès inria-00117178v1
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MERL: Multi-Head Reinforcement Learning

 Yannis Flet-Berliac , Philippe Preux
Deep Reinforcement Learning Workshop, NeurIPS, Dec 2019, Vancouver, Canada
Communication dans un congrès hal-02305105v3

Classification Localement Parcimonieuse par Méthodes Séquentielles

 Gabriel Dulac-Arnold , Ludovic Denoyer , Philippe Preux , Patrick Gallinari
CAP 2012 - Conférence Francophone sur l'Apprentissage Automatique, May 2012, Nancy, France
Communication dans un congrès hal-01357567v1
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Planning-based Approach for Optimizing the Display of Online Advertising Campaigns

 Sertan Girgin , Jérémie Mary , Philippe Preux , Olivier Nicol
NIPS workshop on Machine Learning in Online ADvertising, Dec 2010, Whistler, Canada
Communication dans un congrès hal-00772512v1

A comparison of two machine learning approaches for Photometric Solids Compression

 Delepoulle Samuel , François Rouselle , Renaud Christophe , Philippe Preux
Plemenos, Dimitri; Miaoulis, Georgios. Intelligent Computer Graphics, 321, Springer, pp.145-164, 2010, Studies in Computational Intelligence
Chapitre d'ouvrage hal-00826051v1
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Correctness Attraction: A Study of Stability of Software Behavior Under Runtime Perturbation

 Benjamin Danglot , Philippe Preux , Benoit Baudry , Martin Monperrus
Empirical Software Engineering, 2018, 23 (4), pp.2086-2119. ⟨10.1007/s10664-017-9571-8⟩
Article dans une revue hal-01378523v3

Feature Discovery in Approximate Dynamic Programming

 Philippe Preux , Sertan Girgin , Manuel Loth
Approximate Dynamic Programming and Reinforcement Learning, Mar 2009, Nashville, United States
Communication dans un congrès hal-00351144v1

Simultaneous Optimistic Optimization on the Noiseless BBOB Testbed

 Bilel Derbel , Philippe Preux
The 17th IEEE Congress on Evolutionary Computation (CEC), May 2015, Sendai, Japan
Communication dans un congrès hal-01246420v1

A Generative Model of Software Dependency Graphs to Better Understand Software Evolution

 Vincenzo Musco , Martin Monperrus , Philippe Preux
[Technical Report] hal-01078716, Inria. 2014
Rapport hal-01078716v1
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Learning crop management by reinforcement: gym-DSSAT

 Romain Gautron , Emilio J Padrón , Philippe Preux , Julien Bigot , Odalric-Ambrym Maillard , et al.
AIAFS 2023 - 2nd AAAI Workshop on AI for Agriculture and Food Systems, Feb 2023, Washignton DC, United States
Communication dans un congrès hal-03976393v1
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Better state exploration using action sequence equivalence

 Nathan Grinsztajn , Toby Johnstone , Johan Ferret , Philippe Preux
NeurIPS 2022 - Deep Reinforcement Learning Workshop, Dec 2022, Virtual, United States
Communication dans un congrès hal-03920349v1

L’apprentissage automatique : le diable n’est pas dans l’algorithme

 Philippe Preux , Marc Tommasi , Thierry Viéville , Colin de La Higuera
2015
Autre publication scientifique hal-01246178v1
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A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation

 Crícia Z Felício , Klérisson V R Paixão , Celia a Z Barcelos , Philippe Preux
25th ACM Conference on User Modelling, Adaptation and Personalization (UMAP), Jul 2017, Bratislava, Slovakia
Communication dans un congrès hal-01517967v1
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The Iso-regularization Descent Algorithm for the LASSO

 Manuel Loth , Philippe Preux
17th International Conference on Neural Information Processing, Nov 2010, Sidney, Australia
Communication dans un congrès inria-00508257v2
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General Framework for Nonlinear Functional Regression with Reproducing Kernel Hilbert Spaces

 Hachem Kadri , Emmanuel Duflos , Manuel Davy , Philippe Preux , Stephane Canu
[Research Report] RR-6908, INRIA. 2009
Rapport inria-00378381v1
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Multiple functional regression with both discrete and continuous covariates

 Hachem Kadri , Philippe Preux , Emmanuel Duflos , Stéphane Canu
2nd International Workshop on Functional and Operatorial Statistics (IWFOS), Jun 2011, Santander, Spain. pp.189-195
Communication dans un congrès hal-00772425v1
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Functional Regularized Least Squares Classi cation with Operator-valued Kernels

 Hachem Kadri , Asma Rabaoui , Philippe Preux , Emmanuel Duflos , Alain Rakotomamonjy
28th International Conference on Machine Learning (ICML), Jun 2011, Seattle, United States. pp.993--1000
Communication dans un congrès hal-00772406v1

Recent Advances in Reinforcement Learning

 Sertan Girgin , Manuel Loth , Rémi Munos , Philippe Preux , Daniil Ryabko
Springer, Lectures Notes in Artificial Intelligence (LNAI), vol. 5323, pp.281, 2009
Ouvrages hal-00351128v1
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Learning HJB Viscosity Solutions with PINNs for Continuous-Time Reinforcement Learning

 Alena Shilova , Thomas Delliaux , Philippe Preux , Bruno Raffin
RR-9541, Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189; Univ. Lille, CNRS, Centrale Lille, Inria UMR 9189 - CRIStAL,INRIA Lille Nord Europe, Villeneuve d’Ascq, France; Univ. Grenoble Alps, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France. 2024, pp.1-30
Rapport hal-04445160v1
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