Incremental Learning of Rules and Meta-rules. - Marc Schoenauer; Michèle Sebag (ML, 1990)

Using Examples to Refine a Redundant Knowledge Base. - Michèle Sebag; Marc Schoenauer (EUROVAV, 1991)

Learning to Control Inconsistent Knowledge. - Michèle Sebag; Marc Schoenauer (ECAI, 1992)

A Rule-Based Similarity Measure. - Michèle Sebag; Marc Schoenauer (EWCBR, 1993)

Using Constraints to Building Version Spaces. - Michèle Sebag (ECML, 1994)

A Constraint-based Induction Algorithm in FOL. - Michèle Sebag (ICML, 1994)

Controlling Crossover through Inductive Learning. - Michèle Sebag; Marc Schoenauer (PPSN, 1994)

Induction-Based Control of Genetic Algorithms. - Caroline Ravise; Michèle Sebag; Marc Schoenauer (Artificial Evolution, 1995)

An Induction-based Control for Genetic Algorithms (Extended Abstract). - Michèle Sebag; Marc Schoenauer; Caroline Ravise (ECML, 1995)

A numerical strategy to defectuous knowledge using. - Michèle Sebag; Marc Schoenauer (Ann. Oper. Res., 1995)

Controlling Evolution by Means of Machine Learning. - Michèle Sebag; Caroline Ravise; Marc Schoenauer (Evolutionary Programming, 1996)

An Advanced Evolution Should Not Repeat its Past Errors. - Caroline Ravise; Michèle Sebag (ICML, 1996)

Delaying the Choice of Bias: A Disjunctive Version Space Approach. - Michèle Sebag (ICML, 1996)

Polynomial-Time Learning in Logic Programming and Constraint Logic Programming. - Michèle Sebag; Céline Rouveirol (Inductive Logic Programming Workshop, 1996)

Mutation by Imitation in Boolean Evolution Strategies. - Michèle Sebag; Marc Schoenauer (PPSN, 1996)

Induction of Constraint Logic Programs. - Michèle Sebag; Céline Rouveirol; Jean-Francois Puget (PRICAI Workshops, 1996)

Mimetic Evolution. - Mathieu Peyral; Antoine Ducoulombier; Caroline Ravise; Marc Schoenauer; Michèle Sebag (Artificial Evolution, 1997)

Inductive Learning of Mutation Step-Size in Evolutionary Parameter Optimization. - Michèle Sebag; Marc Schoenauer; Caroline Ravise (Evolutionary Programming, 1997)

A society of hill-climbers. - Michèle Sebag; Marc Schoenauer (ICEC, 1997)

Toward Civilized Evolution: Developing Inhibitions. - Michèle Sebag; Marc Schoenauer; Caroline Ravise (ICGA, 1997)

Tractable Induction and Classification in First Order Logic Via Stochastic Matching. - Michèle Sebag; Céline Rouveirol (IJCAI (2), 1997)

Distance Induction in First Order Logic. - Michèle Sebag (ILP, 1997)

Continuous Mimetic Evolution. - Antoine Ducoulombier; Michèle Sebag (ECML, 1998)

A Stochastic Simple Similarity. - Michèle Sebag (ILP, 1998)

Extending Population-Based Incremental Learning to Continuous Search Spaces. - Michèle Sebag; Antoine Ducoulombier (PPSN, 1998)

Revisiting the Memory of Evolution. - Michèle Sebag; Marc Schoenauer; Mathieu Peyral (Fundam. Informaticae, 1998)

Twelve Numerical, Symbolic and Hybrid Supervised Classification Methods. - Olivier Gascuel; Bernadette Bouchon-Meunier; Gilles Caraux; Patrick Gallinari; Alain Guénoche; Yann Guermeur; Yves Lechevallier; Christophe Marsala; Laurent Miclet; Jacques Nicolas; Richard Nock; Mohammed Ramdani 003; et al. (Int. J. Pattern Recognit. Artif. Intell., 1998)

Relational Learning: Hard Problems and Phase Transitions. - Marco Botta; Attilio Giordana; Lorenza Saitta; Michèle Sebag (AI*IA, 1999)

From first order logic to Nd: a data driven reformulation. - Michèle Sebag (ESANN, 1999)

Constructive Induction: A Version Space-based Approach. - Michèle Sebag (IJCAI, 1999)

Hacia un Enfoque General del Trazado de Grafos. - Alejandro Rosete-Suárez; Alberto Nogueira-Keeling; Alberto Ochoa-Rodríguez; Michèle Sebag (Inteligencia Artif., 1999)

A Distributed Resource Evolutionary Algorithm Machine (DREAM). - Ben Paechter; Thomas Bäck; Marc Schoenauer; Michèle Sebag; Ágoston E. Eiben; Juan Julián Merelo; Terence C. Fogarty (CEC, 2000)

Analyzing Relational Learning in the Phase Transition Framework. - Attilio Giordana; Lorenza Saitta; Michèle Sebag; Marco Botta (ICML, 2000)

Can Relational Learning Scale Up? - Attilio Giordana; Lorenza Saitta; Michèle Sebag; Marco Botta (ISMIS, 2000)

Genetic Programming and Domain Knowledge: Beyond the Limitations of Grammar-Guided Machine Discovery. - Alain Ratle; Michèle Sebag (PPSN, 2000)

Any-time Relational Reasoning: Resource-bounded Induction and Deduction Through Stochastic Matching. - Michèle Sebag; Céline Rouveirol (Mach. Learn., 2000)

Avoiding the Bloat with Stochastic Grammar-Based Genetic Programming. - Alain Ratle; Michèle Sebag (Artificial Evolution, 2001)

Combining Statistics and Semantics for Word and Document Clustering. - Alexandre Termier; Michèle Sebag; Marie-Christine Rousset (Workshop on Ontology Learning, 2001)

Theta-Subsumption in a Constraint Satisfaction Perspective. - Jérôme Maloberti; Michèle Sebag (ILP, 2001)

Grammar-guided genetic programming and dimensional consistency: application to non-parametric identification in mechanics. - Alain Ratle; Michèle Sebag (Appl. Soft Comput., 2001)

TreeFinder: a First Step towards XML Data Mining. - Alexandre Termier; Marie-Christine Rousset; Michèle Sebag (ICDM, 2002)

Constraint-based Learning of Long Relational Concepts. - Jacques Ales Bianchetti; Céline Rouveirol; Michèle Sebag (ICML, 2002)

A Novel Approach to Machine Discovery: Genetic Programming and Stochastic Grammars. - Alain Ratle; Michèle Sebag (ILP, 2002)

Compact Unstructured Representations for Evolutionary Design. - Hatem Hamda; François Jouve; Evelyne Lutton; Marc Schoenauer; Michèle Sebag (Appl. Intell., 2002)

ROC-Based Evolutionary Learning: Application to Medical Data Mining. - Michèle Sebag; Jérôme Azé; Noël Lucas (Artificial Evolution, 2003)

Evolving Symbolic Controllers. - Nicolas Godzik; Marc Schoenauer; Michèle Sebag (EvoWorkshops, 2003)

Fouille de données visuelle et analyse de facteurs de risque médical. - Jérôme Azé; Noël Lucas; Michèle Sebag (EGC, 2003)

Nouveaux résultats en classification à l'aide d'un codage par motifs fréquents. - Sébastien Jouteau; Antoine Cornuéjols; Michèle Sebag; Philippe Tarroux; Jean-Sylvain Liénard (EGC, 2003)

Impact Studies and Sensitivity Analysis in Medical Data Mining with ROC-based Genetic Learning. - Michèle Sebag; Jérôme Azé; Noël Lucas (ICDM, 2003)

Relational Learning as Search in a Critical Region. - Marco Botta; Attilio Giordana; Lorenza Saitta; Michèle Sebag (J. Mach. Learn. Res., 2003)

Scalability and efficiency in multi-relational data mining. - Hendrik Blockeel; Michèle Sebag (SIGKDD Explor., 2003)

Feature selection in proteomic pattern data with support vector machines. - Kees Jong; Elena Marchiori; Michèle Sebag; Aad van der Vaart (CIBCB, 2004)

DRYADE: A New Approach for Discovering Closed Frequent Trees in Heterogeneous Tree Databases. - Alexandre Termier; Marie-Christine Rousset; Michèle Sebag (ICDM, 2004)

C4.5 competence map: a phase transition-inspired approach. - Nicolas Baskiotis; Michèle Sebag (ICML, 2004)

Learning to Order Terms: Supervised Interestingness Measures in Terminology Extraction - Jérôme Azé; Mathieu Roche; Yves Kodratoff; Michèle Sebag (International Conference on Computational Intelligence, 2004)

Ensemble Feature Ranking. - Kees Jong; Jérémie Mary; Antoine Cornuéjols; Elena Marchiori; Michèle Sebag (PKDD, 2004)

Robotics and Multi-agent Systems Robustness in the Long Run: Auto-teaching vs Anticipation in Evolutionary Robotics. - Nicolas Godzik; Marc Schoenauer; Michèle Sebag (PPSN, 2004)

Ensemble Learning with Evolutionary Computation: Application to Feature Ranking. - Kees Jong; Elena Marchiori; Michèle Sebag (PPSN, 2004)

Learning Interestingness Measures in Terminology Extraction. A ROC-based approach. - Mathieu Roche; Jérôme Azé; Yves Kodratoff; Michèle Sebag (ROCAI, 2004)

Fast Theta-Subsumption with Constraint Satisfaction Algorithms. - Jérôme Maloberti; Michèle Sebag (Mach. Learn., 2004)

Systèmes inductifs-déductifs: une approche statistique. - Nicolas Baskiotis; Michèle Sebag; Olivier Teytaud (CAP, 2005)

HMM hiérarchiques et factorisés: mécanisme d'inférence et apprentissage à partir de peu de données. - Sylvain Gelly; Nicolas Bredèche; Michèle Sebag (CAP, 2005)

Phase transitions in grammatical inference. - Nicolas Pernot; Antoine Cornuéjols; Michèle Sebag (CAP, 2005)

Multi-objective Multi-modal Optimization for Mining Spatio-temporal Patterns. - Nicolas Tarrisson; Michèle Sebag; Olivier Teytaud; Julien Lefèvre; Sylvain Baillet (CAP, 2005)

Bayesian Learning with Local Support Vector Machines for Cancer Classification with Gene Expression Data. - Elena Marchiori; Michèle Sebag (EvoWorkshops, 2005)

Inférence dans les HMM hiérarchiques et factorisés : changement de représentation vers le formalisme des Réseaux Bayésiens. - Sylvain Gelly; Michèle Sebag; Nicolas Bredèche (EGC (Ateliers), 2005)

Efficient Mining of High Branching Factor Attribute Trees. - Alexandre Termier; Marie-Christine Rousset; Michèle Sebag; Kouzou Ohara; Takashi Washio; Hiroshi Motoda (ICDM, 2005)

Phase Transitions within Grammatical Inference. - Nicolas Pernot; Antoine Cornuéjols; Michèle Sebag (IJCAI, 2005)

A Multi-Objective Multi-Modal Optimization Approach for Mining Stable Spatio-Temporal Patterns. - Michèle Sebag; Nicolas Tarrisson; Olivier Teytaud; Julien Lefèvre; Sylvain Baillet (IJCAI, 2005)

From Factorial and Hierarchical HMM to Bayesian Network: A Representation Change Algorithm. - Sylvain Gelly; Nicolas Bredèche; Michèle Sebag (SARA, 2005)

Artificial Agents and Speculative Bubbles - Yann Semet; Sylvain Gelly; Marc Schoenauer; Michèle Sebag (CoRR, 2005)

Preference Learning in Terminology Extraction: A ROC-based approach - Jérôme Azé; Mathieu Roche; Yves Kodratoff; Michèle Sebag (CoRR, 2005)

Genetic Programming for Kernel-Based Learning with Co-evolving Subsets Selection. - Christian Gagné 001; Marc Schoenauer; Michèle Sebag; Marco Tomassini (PPSN, 2006)

Functional Brain Imaging with Multi-objective Multi-modal Evolutionary Optimization. - Vojtech Krmicek; Michèle Sebag (PPSN, 2006)

Using Domain Knowledge in Evolutionary System Identification - Marc Schoenauer; Michèle Sebag (CoRR, 2006)

Structural Sampling for Statistical Software Testing. - Nicolas Baskiotis; Michèle Sebag (Probabilistic, Logical and Relational Learning - A Further Synthesis, 2007)

Ensemble learning for free with evolutionary algorithms? - Christian Gagné 001; Michèle Sebag; Marc Schoenauer; Marco Tomassini (GECCO, 2007)

Toward Behavioral Modeling of a Grid System: Mining the Logging and Bookkeeping Files. - Xiangliang Zhang 001; Michèle Sebag; Cécile Germain (ICDM Workshops, 2007)

A Machine Learning Approach for Statistical Software Testing. - Nicolas Baskiotis; Michèle Sebag; Marie-Claude Gaudel; Sandrine-Dominique Gouraud (IJCAI, 2007)

Structural Statistical Software Testing with Active Learning in a Graph. - Nicolas Baskiotis; Michèle Sebag (ILP, 2007)

A Phase Transition-Based Perspective on Multiple Instance Kernels. - Romaric Gaudel; Michèle Sebag; Antoine Cornuéjols (ILP, 2007)

SoftwareTesting with Active Learning in a Graph. - Nicolas Baskiotis; Michèle Sebag; Marie-Claude Gaudel (Evolutionary Test Generation, 2008)

Adaptive operator selection with dynamic multi-armed bandits. - Luís Da Costa; Álvaro Fialho; Marc Schoenauer; Michèle Sebag (GECCO, 2008)

Data Streaming with Affinity Propagation. - Xiangliang Zhang 001; Cyril Furtlehner; Michèle Sebag (ECML/PKDD (2), 2008)

Extreme Value Based Adaptive Operator Selection. - Álvaro Fialho; Luís Da Costa; Marc Schoenauer; Michèle Sebag (PPSN, 2008)

Distributed and Incremental Clustering Based on Weighted Affinity Propagation. - Xiangliang Zhang 001; Cyril Furtlehner; Michèle Sebag (STAIRS, 2008)

A note on phase transitions and computational pitfalls of learning from sequences. - Antoine Cornuéjols; Michèle Sebag (J. Intell. Inf. Syst., 2008)

DryadeParent, An Efficient and Robust Closed Attribute Tree Mining Algorithm. - Alexandre Termier; Marie-Christine Rousset; Michèle Sebag; Kouzou Ohara; Takashi Washio; Hiroshi Motoda (IEEE Trans. Knowl. Data Eng., 2008)

Multi-scale Real-Time Grid Monitoring with Job Stream Mining. - Xiangliang Zhang 001; Michèle Sebag; Cécile Germain-Renaud (CCGRID, 2009)

Memory-enhanced Evolutionary Robotics: The Echo State Network Approach. - Cédric Hartland; Nicolas Bredèche; Michèle Sebag (IEEE Congress on Evolutionary Computation, 2009)

Extreme compass and Dynamic Multi-Armed Bandits for Adaptive Operator Selection. - Jorge Maturana; Álvaro Fialho; Frédéric Saubion; Marc Schoenauer; Michèle Sebag (IEEE Congress on Evolutionary Computation, 2009)

Extreme: dynamic multi-armed bandits for adaptive operator selection. - Álvaro Fialho; Luís Da Costa; Marc Schoenauer; Michèle Sebag (GECCO (Companion), 2009)

Analysis of adaptive operator selection techniques on the royal road and long k-path problems. - Álvaro Fialho; Marc Schoenauer; Michèle Sebag (GECCO, 2009)

Optimal robust expensive optimization is tractable. - Philippe Rolet; Michèle Sebag; Olivier Teytaud (GECCO, 2009)

Toward autonomic grids: analyzing the job flow with affinity streaming. - Xiangliang Zhang 001; Cyril Furtlehner; Julien Perez; Cécile Germain-Renaud; Michèle Sebag (KDD, 2009)

Dynamic Multi-Armed Bandits and Extreme Value-Based Rewards for Adaptive Operator Selection in Evolutionary Algorithms. - Álvaro Fialho; Luís Da Costa; Marc Schoenauer; Michèle Sebag (LION, 2009)

Boosting Active Learning to Optimality: A Tractable Monte-Carlo, Billiard-Based Algorithm. - Philippe Rolet; Michèle Sebag; Olivier Teytaud (ECML/PKDD (2), 2009)

Scaling Analysis of Affinity Propagation - Cyril Furtlehner; Michèle Sebag; Xiangliang Zhang 001 (CoRR, 2009)

Discovering Piecewise Linear Models of Grid Workload. - Tamás Éltetö; Cécile Germain-Renaud; Pascal Bondon; Michèle Sebag (CCGRID, 2010)

Unsupervised Layer-Wise Model Selection in Deep Neural Networks. - Ludovic Arnold; Hélène Paugam-Moisy; Michèle Sebag (ECAI, 2010)

Toward comparison-based adaptive operator selection. - Álvaro Fialho; Marc Schoenauer; Michèle Sebag (GECCO, 2010)

Fitness-AUC bandit adaptive strategy selection vs. the probability matching one within differential evolution: an empirical comparison on the bbob-2010 noiseless testbed. - Álvaro Fialho; Marc Schoenauer; Michèle Sebag (GECCO (Companion), 2010)

A mono surrogate for multiobjective optimization. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (GECCO, 2010)

A pareto-compliant surrogate approach for multiobjective optimization. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (GECCO (Companion), 2010)

Adaptively detecting changes in Autonomic Grid Computing. - Xiangliang Zhang 001; Cécile Germain; Michèle Sebag (GRID, 2010)

K-AP: Generating Specified K Clusters by Efficient Affinity Propagation. - Xiangliang Zhang 001; Wei Wang 012; Kjetil Nørvåg; Michèle Sebag (ICDM, 2010)

Building Portfolios for the Protein Structure Prediction Problem. - Alejandro Arbelaez; Youssef Hamadi; Michèle Sebag (WCB@ICLP, 2010)

Feature Selection as a One-Player Game. - Romaric Gaudel; Michèle Sebag (ICML, 2010)

Continuous Search in Constraint Programming. - Alejandro Arbelaez; Youssef Hamadi; Michèle Sebag (ICTAI (1), 2010)

SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system. - Sylvain Chevallier; Hélène Paugam-Moisy; Michèle Sebag (NIPS, 2010)

Open-Ended Evolutionary Robotics: An Information Theoretic Approach. - Pierre Delarboulas; Marc Schoenauer; Michèle Sebag (PPSN (1), 2010)

Comparison-Based Adaptive Strategy Selection with Bandits in Differential Evolution. - Álvaro Fialho; Raymond Ros; Marc Schoenauer; Michèle Sebag (PPSN (1), 2010)

Comparison-Based Optimizers Need Comparison-Based Surrogates. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (PPSN (1), 2010)

Dominance-Based Pareto-Surrogate for Multi-Objective Optimization. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (SEAL, 2010)

Analyzing bandit-based adaptive operator selection mechanisms. - Álvaro Fialho; Luís Da Costa; Marc Schoenauer; Michèle Sebag (Ann. Math. Artif. Intell., 2010)

Guest editors' introduction: special issue of selected papers from ECML PKDD 2010. - José L. Balcázar; Francesco Bonchi; Aristides Gionis; Michèle Sebag (Data Min. Knowl. Discov., 2010)

Special issue for ECML PKDD 2010: Guest editors' introduction. - José L. Balcázar; Francesco Bonchi; Aristides Gionis; Michèle Sebag (Mach. Learn., 2010)

Grammatical Inference. - Lorenza Saitta; Michèle Sebag (Encyclopedia of Machine Learning, 2010)

Phase Transitions in Machine Learning. - Lorenza Saitta; Michèle Sebag (Encyclopedia of Machine Learning, 2010)

Nonstandard Criteria in Evolutionary Learning. - Michèle Sebag (Encyclopedia of Machine Learning, 2010)

The Grid Observatory. - Cécile Germain-Renaud; Alain Cady; Philippe Gauron; Michel Jouvin; Charles Loomis; Janusz Martyniak; Julien Nauroy; Guillaume Philippon; Michèle Sebag (CCGRID, 2011)

Emergence of temporal and spatial synchronous behaviors in a foraging swarm. - Sylvain Chevallier; Nicolas Bredèche; Hélène Paugam-Moisy; Michèle Sebag (ECAL, 2011)

Not All Parents Are Equal for MO-CMA-ES. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (EMO, 2011)

Adaptive coordinate descent. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (GECCO, 2011)

On-Board Evolutionary Algorithm and Off-Line Rule Discovery for Column Formation in Swarm Robotics. - Asuki Kouno; Jean-Marc Montanier; Shigeru Takano; Nicolas Bredèche; Marc Schoenauer; Michèle Sebag; Einoshin Suzuki (IAT, 2011)

Characterizing Anomalous Behaviors and Revising Robotic Controllers. - David Meunier; Michèle Sebag; Shin Ando (ICDM Workshops, 2011)

Using SVM to Avoid Humans: A Case of a Small Autonomous Mobile Robot in an Office. - Emi Matsumoto; Michèle Sebag; Einoshin Suzuki (ISCIS, 2011)

Preference-Based Policy Learning. - Riad Akrour; Marc Schoenauer; Michèle Sebag (ECML/PKDD (1), 2011)

Towards Non-Stationary Grid Models. - Tamás Éltetö; Cécile Germain-Renaud; Pascal Bondon; Michèle Sebag (J. Grid Comput., 2011)

Single trial variability in brain-computer interfaces based on motor imagery: Learning in the presence of labeling noise. - Cédric Gouy-Pailler; Michèle Sebag; Anthony Larue; Antoine Souloumiac (Int. J. Imaging Syst. Technol., 2011)

Continuous RAVE. - Adrien Couëtoux; Mario Milone; Mátyás Brendel; Hassen Doghmen; Michèle Sebag; Olivier Teytaud (ACML, 2011)

Continuous Search in Constraint Programming. - Alejandro Arbelaez; Youssef Hamadi; Michèle Sebag (Autonomous Search, 2012)

Adaptive Operator Selection and Management in Evolutionary Algorithms. - Jorge Maturana; Álvaro Fialho; Frédéric Saubion; Marc Schoenauer; Frédéric Lardeux; Michèle Sebag (Autonomous Search, 2012)

Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (GECCO, 2012)

Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (GECCO (Companion), 2012)

Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (GECCO (Companion), 2012)

Black-box optimization benchmarking of NIPOP-aCMA-ES and NBIPOP-aCMA-ES on the BBOB-2012 noiseless testbed. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (GECCO (Companion), 2012)

BenchNN: On the broad potential application scope of hardware neural network accelerators. - Tianshi Chen 002; Yunji Chen; Marc Duranton; Qi Guo 001; Atif Hashmi; Mikko H. Lipasti; Andrew Nere; Shi Qiu; Michèle Sebag; Olivier Temam (IISWC, 2012)

Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling. - Thomas Philip Runarsson; Marc Schoenauer; Michèle Sebag (LION, 2012)

Upper Confidence Tree-Based Consistent Reactive Planning Application to MineSweeper. - Michèle Sebag; Olivier Teytaud (LION, 2012)

APRIL: Active Preference Learning-Based Reinforcement Learning. - Riad Akrour; Marc Schoenauer; Michèle Sebag (ECML/PKDD (2), 2012)

Alternative Restart Strategies for CMA-ES. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (PPSN (1), 2012)

The grand challenge of computer Go: Monte Carlo tree search and extensions. - Sylvain Gelly; Levente Kocsis; Marc Schoenauer; Michèle Sebag; David Silver; Csaba Szepesvári; Olivier Teytaud (Commun. ACM, 2012)

Multi-objective Monte-Carlo Tree Search. - Weijia Wang 001; Michèle Sebag (ACML, 2012)

Exploration vs Exploitation vs Safety: Risk-Aware Multi-Armed Bandits. - Nicolas Galichet; Michèle Sebag; Olivier Teytaud (ACML, 2013)

Fast Adaptive Object Detection towards a Smart Environment by a Mobile Robot. - Shigeru Takano; Ilya Loshchilov; David Meunier; Michèle Sebag; Einoshin Suzuki (AmI, 2013)

Bandit-Based Search for Constraint Programming. - Manuel Loth; Michèle Sebag; Youssef Hamadi; Marc Schoenauer (CP, 2013)

Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es). - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (GECCO, 2013)

Bi-population CMA-ES agorithms with surrogate models and line searches. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (GECCO (Companion), 2013)

Sustainable cooperative coevolution with a multi-armed bandit. - François-Michel De Rainville; Michèle Sebag; Christian Gagné 001; Marc Schoenauer; Denis Laurendeau (GECCO, 2013)

Multi-dimensional sparse structured signal approximation using split bregman iterations. - Yoann Isaac; Quentin Barthélemy; Jamal Atif; Cédric Gouy-Pailler; Michèle Sebag (ICASSP, 2013)

Collaborative hyperparameter tuning. - Rémi Bardenet; Mátyás Brendel; Balázs Kégl; Michèle Sebag (ICML (2), 2013)

Hybridizing Constraint Programming and Monte-Carlo Tree Search: Application to the Job Shop Problem. - Manuel Loth; Michèle Sebag; Youssef Hamadi; Marc Schoenauer; Christian Schulte 001 (LION, 2013)

KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag (CoRR, 2013)

Hypervolume indicator and dominance reward based multi-objective Monte-Carlo Tree Search. - Weijia Wang 001; Michèle Sebag (Mach. Learn., 2013)

A Recommender System for Process Discovery. - Joel Ribeiro; Josep Carmona 001; Mustafa Misir; Michèle Sebag (BPM, 2014)

Programming by Feedback. - Marc Schoenauer; Riad Akrour; Michèle Sebag; Jean-Christophe Souplet (ICML, 2014)

Combination of One-Class Support Vector Machines for Classification with Reject Option. - Blaise Hanczar; Michèle Sebag (ECML/PKDD (1), 2014)

Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach. - Artémis Llamosi; Adel Mezine; Florence d'Alché-Buc; Véronique Letort; Michèle Sebag (ECML/PKDD (2), 2014)

Maximum Likelihood-Based Online Adaptation of Hyper-Parameters in CMA-ES. - Ilya Loshchilov; Marc Schoenauer; Michèle Sebag; Nikolaus Hansen (PPSN, 2014)

Coupling Evolution and Information Theory for Autonomous Robotic Exploration. - Guohua Zhang; Michèle Sebag (PPSN, 2014)

A tour of machine learning: An AI perspective. - Michèle Sebag (AI Commun., 2014)

Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits. - Nicolas Galichet; Michèle Sebag; Olivier Teytaud (CoRR, 2014)

Constraints, Optimization and Data (Dagstuhl Seminar 14411). - Luc De Raedt; Siegfried Nijssen; Barry O'Sullivan; Michèle Sebag (Dagstuhl Reports, 2014)

Strategic Choices in Optimization. - Cheng-Wei Chou; Ping-Chiang Chou; Jean-Joseph Christophe; Adrien Couëtoux; Pierre de Freminville; Nicolas Galichet; Chang-Shing Lee; Jialin Liu 001; David Lupien Saint-Pierre; Michèle Sebag; Olivier Teytaud; Mei-Hui Wang; et al. (J. Inf. Sci. Eng., 2014)

Data Stream Clustering With Affinity Propagation. - Xiangliang Zhang 001; Cyril Furtlehner; Cécile Germain-Renaud; Michèle Sebag (IEEE Trans. Knowl. Data Eng., 2014)

Learning Sparse Features with an Auto-Associator. - Sébastien Rebecchi; Hélène Paugam-Moisy; Michèle Sebag (Growing Adaptive Machines, 2014)

Collaborative Algorithm Platforms. - Michèle Sebag (DATA, 2015)

Interactive Metric Learning-Based Visual Data Exploration: Application to the Visualization of a Scientific Social Network. - Masaharu Yoshioka; Masahiko Itoh; Michèle Sebag (ISIP, 2015)

Matching Jobs and Resumes: a Deep Collaborative Filtering Task. - Thomas Schmitt 001; Phillipe Caillou; Michèle Sebag (GCAI, 2016)

A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention. - Isabelle Guyon; Imad Chaabane; Hugo Jair Escalante; Sergio Escalera; Damir Jajetic; James Robert Lloyd; Núria Macià; Bisakha Ray; Lukasz Romaszko; Michèle Sebag; Alexander R. Statnikov; Sébastien Treguer; et al. (AutoML@ICML, 2016)

Anti Imitation-Based Policy Learning. - Michèle Sebag; Riad Akrour; Basile Mayeur; Marc Schoenauer (ECML/PKDD (2), 2016)

Language Modelling for Collaborative Filtering: Application to Job Applicant Matching. - Thomas Schmitt 001; François Gonard; Phillipe Caillou; Michèle Sebag (ICTAI, 2017)

ASAP.V2 and ASAP.V3: Sequential optimization of an Algorithm Selector and a Scheduler. - François Gonard; Marc Schoenauer; Michèle Sebag (OASC, 2017)

Alors: An algorithm recommender system. - Mustafa Misir; Michèle Sebag (Artif. Intell., 2017)

Toward Optimal Run Racing: Application to Deep Learning Calibration. - Olivier Bousquet; Sylvain Gelly; Karol Kurach; Marc Schoenauer; Michèle Sebag; Olivier Teytaud; Damien Vincent (CoRR, 2017)

Stochastic Gradient Descent: Going As Fast As Possible But Not Faster. - Alice Schoenauer Sebag; Marc Schoenauer; Michèle Sebag (CoRR, 2017)

Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations. - Yoann Isaac; Quentin Barthélemy; Cédric Gouy-Pailler; Michèle Sebag; Jamal Atif (Signal Process., 2017)

Grammatical Inference. - Lorenza Saitta; Michèle Sebag (Encyclopedia of Machine Learning and Data Mining, 2017)

Phase Transitions in Machine Learning. - Lorenza Saitta; Michèle Sebag (Encyclopedia of Machine Learning and Data Mining, 2017)

Nonstandard Criteria in Evolutionary Learning. - Michèle Sebag (Encyclopedia of Machine Learning and Data Mining, 2017)

ActivMetal: Algorithm Recommendation with Active Meta Learning. - Lisheng Sun-Hosoya; Isabelle Guyon; Michèle Sebag (IAL@PKDD/ECML, 2018)

Learning Bivariate Functional Causal Models. - Olivier Goudet; Diviyan Kalainathan; Michèle Sebag; Isabelle Guyon (Cause Effect Pairs in Machine Learning, 2019)

Analysis of the AutoML Challenge Series 2015-2018. - Isabelle Guyon; Lisheng Sun-Hosoya; Marc Boullé; Hugo Jair Escalante; Sergio Escalera; Zhengying Liu; Damir Jajetic; Bisakha Ray; Mehreen Saeed; Michèle Sebag; Alexander R. Statnikov; Wei-Wei Tu; et al. (Automated Machine Learning, 2019)

Discriminant Learning Machines. - Diviyan Kalainathan; Olivier Goudet; Michèle Sebag; Isabelle Guyon (Cause Effect Pairs in Machine Learning, 2019)

Multi-Domain Adversarial Learning. - Alice Schoenauer Sebag; Louise Heinrich; Marc Schoenauer; Michèle Sebag; Lani F. Wu; Steven J. Altschuler (ICLR (Poster), 2019)

Automated Machine Learning with Monte-Carlo Tree Search. - Herilalaina Rakotoarison; Marc Schoenauer; Michèle Sebag (IJCAI, 2019)

From Abstract Items to Latent Spaces to Observed Data and Back: Compositional Variational Auto-Encoder. - Victor Berger; Michèle Sebag (ECML/PKDD (1), 2019)

Agnostic Feature Selection. - Guillaume Doquet; Michèle Sebag (ECML/PKDD (1), 2019)

Automated Machine Learning with Monte-Carlo Tree Search (Extended Version). - Herilalaina Rakotoarison; Marc Schoenauer; Michèle Sebag (CoRR, 2019)

Towards AutoML in the presence of Drift: first results. - Jorge G. Madrid; Hugo Jair Escalante; Eduardo F. Morales 001; Wei-Wei Tu; Yang Yu 001; Lisheng Sun-Hosoya; Isabelle Guyon; Michèle Sebag (CoRR, 2019)

Dynamic Time Lag Regression: Predicting What & When. - Mandar Chandorkar; Cyril Furtlehner; Bala Poduval; Enrico Camporeale; Michèle Sebag (ICLR, 2020)

Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals. - Roman Bresson; Johanne Cohen; Eyke Hüllermeier; Christophe Labreuche; Michèle Sebag (IJCAI, 2020)

Variational Auto-Encoder: not all failures are equal. - Victor Berger; Michèle Sebag (CoRR, 2020)

Distribution-Based Invariant Deep Networks for Learning Meta-Features. - Gwendoline de Bie; Herilalaina Rakotoarison; Gabriel Peyré; Michèle Sebag (CoRR, 2020)

Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities. - Remy Kusters; Dusan Misevic; Hugues Berry; Antoine Cully; Yann Le Cunff; Loic Dandoy; Natalia Díaz Rodríguez; Marion Ficher; Jonathan Grizou; Alice Othmani; Themis Palpanas; Matthieu Komorowski; et al. (Frontiers Big Data, 2020)

Towards causal modeling of nutritional outcomes. - Ksenia Gasnikova; Olivier Allais; Michèle Sebag (CAWS, 2021)

Important Topics in Causal Analysis: Summary of the CAWS 2021 Round Table Discussion. - Erich Kummerfeld; Thomas Woolf; Will Glad; Michèle Sebag; Sisi Ma (CAWS, 2021)

I2SL: Learn How to Swarm Autonomous Quadrotors Using Iterative Imitation Supervised Learning. - Omar Shrit; Michèle Sebag (EPIA, 2021)

Iterative Learning for Model Reactive Control: Application to Autonomous Multi-agent Control. - Omar Shrit; David Filliat; Michèle Sebag (ICARA, 2021)

On the Identifiability of Hierarchical Decision Models. - Roman Bresson; Johanne Cohen; Eyke Hüllermeier; Christophe Labreuche; Michèle Sebag (KR, 2021)

Cartolabe: A Web-Based Scalable Visualization of Large Document Collections. - Philippe Caillou; Jonas Renault; Jean-Daniel Fekete; Anne-Catherine Letournel; Michèle Sebag (IEEE Computer Graphics and Applications, 2021)

Boltzmann Tuning of Generative Models. - Victor Berger; Michèle Sebag (CoRR, 2021)

Frugal Machine Learning. - Mikhail Evchenko; Joaquin Vanschoren; Holger H. Hoos; Marc Schoenauer; Michèle Sebag (CoRR, 2021)

Learning meta-features for AutoML. - Herilalaina Rakotoarison; Louisot Milijaona; Andry Rasoanaivo; Michèle Sebag; Marc Schoenauer (ICLR, 2022)

From Graphs to DAGs: A Low-Complexity Model and a Scalable Algorithm. - Shuyu Dong; Michèle Sebag (ECML/PKDD (5), 2022)

High-Dimensional Causal Discovery: Learning from Inverse Covariance via Independence-based Decomposition. - Shuyu Dong; Kento Uemura; Akito Fujii; Shuang Chang; Yusuke Koyanagi; Koji Maruhashi; Michèle Sebag (CoRR, 2022)

Structural Agnostic Modeling: Adversarial Learning of Causal Graphs. - Diviyan Kalainathan; Olivier Goudet; Isabelle Guyon; David Lopez-Paz; Michèle Sebag (J. Mach. Learn. Res., 2022)

Fairness in job recommendations: estimating, explaining, and reducing gender gaps. - Guillaume Bied; Christophe Gaillac; Morgane Hoffmann; Philippe Caillou; Bruno Crépon; Solal Nathan; Michèle Sebag (AEQUITAS@ECAI, 2023)

RECTO : REcommandation diminuant la Congestion par Transport Optimal. - Guillaume Bied; Elia Perennes; Solal Nathan; Victor Naya; Philippe Caillou; Bruno Crépon; Christophe Gaillac; Michèle Sebag (APIA, 2023)

Toward Job Recommendation for All. - Guillaume Bied; Solal Nathan; Elia Perennes; Morgane Hoffmann; Philippe Caillou; Bruno Crépon; Christophe Gaillac; Michèle Sebag (IJCAI, 2023)

GAN-based data augmentation for transcriptomics: survey and comparative assessment. - Alice Lacan; Michèle Sebag; Blaise Hanczar (Bioinform., 2023)

Cutting the Black Box: Conceptual Interpretation of a Deep Neural Net with Multi-Modal Embeddings and Multi-Criteria Decision Aid. - Nicolas Atienza; Roman Bresson; Cyriaque Rousselot; Philippe Caillou; Johanne Cohen; Christophe Labreuche; Michèle Sebag (IJCAI, 2024)

Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges. - Audrey Poinsot; Alessandro Ferreira Leite; Nicolas Chesneau; Michèle Sebag; Marc Schoenauer (IJCAI, 2024)

Frugal Generative Modeling for Tabular Data. - Alice Lacan; Blaise Hanczar; Michèle Sebag (ECML/PKDD (8), 2024)

Modelling Dynamical Systems: Learning ODEs with No Internal ODE Resolution. - Johanne Cohen; Emmanuel Goutierre; Hayg Guler; Fatios Kapotos; Sida-Bastien Li; Michèle Sebag; Bowen Zhu (RP, 2024)

DCDILP: a distributed learning method for large-scale causal structure learning. - Shuyu Dong; Michèle Sebag; Kento Uemura; Akito Fujii; Shuang Chang; Yusuke Koyanagi; Koji Maruhashi (CoRR, 2024)

DCILP: A Distributed Approach for Large-Scale Causal Structure Learning. - Shuyu Dong; Michèle Sebag; Kento Uemura; Akito Fujii; Shuang Chang; Yusuke Koyanagi; Koji Maruhashi (AAAI, 2025)

Provably Safeguarding a Classifier from OOD and Adversarial Samples. - Nicolas Atienza; Johanne Cohen; Christophe Labreuche; Michèle Sebag (ICLR, 2025)

Provably Safeguarding a Classifier from OOD and Adversarial Samples: an Extreme Value Theory Approach. - Nicolas Atienza; Christophe Labreuche; Johanne Cohen; Michèle Sebag (CoRR, 2025)

Asymmetrical Latent Representation for Individual Treatment Effect Modeling. - Armand Lacombe; Michèle Sebag (CoRR, 2025)