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ORCIDMichèle Sebag
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- affiliation: University of Paris-Sud, Laboratory for Computer Science (LRI), France
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2020 – today
- 2024
[i41]Audrey Poinsot, Alessandro Leite, Nicolas Chesneau, Michèle Sebag, Marc Schoenauer:
Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges. CoRR abs/2405.05025 (2024)- [i40]Shuyu Dong, Michèle Sebag, Kento Uemura, Akito Fujii, Shuang Chang, Yusuke Koyanagi, Koji Maruhashi:
DCDILP: a distributed learning method for large-scale causal structure learning. CoRR abs/2406.10481 (2024) - 2023
- [j28]Alice Lacan, Michèle Sebag, Blaise Hanczar:
GAN-based data augmentation for transcriptomics: survey and comparative assessment. Bioinform. 39(Supplement-1): 111-120 (2023) - [c152]Guillaume Bied, Christophe Gaillac, Morgane Hoffmann, Philippe Caillou, Bruno Crépon, Solal Nathan, Michèle Sebag:
Fairness in job recommendations: estimating, explaining, and reducing gender gaps. AEQUITAS@ECAI 2023 - [c151]Guillaume Bied, Elia Perennes, Solal Nathan, Victor Naya, Philippe Caillou, Bruno Crépon, Christophe Gaillac, Michèle Sebag:
RECTO : REcommandation diminuant la Congestion par Transport Optimal. APIA 2023: 89-98 - [c150]Guillaume Bied, Solal Nathan, Elia Perennes, Morgane Hoffmann, Philippe Caillou, Bruno Crépon, Christophe Gaillac, Michèle Sebag:
Toward Job Recommendation for All. IJCAI 2023: 5906-5914 - 2022
- [j27]Diviyan Kalainathan, Olivier Goudet
, Isabelle Guyon, David Lopez-Paz, Michèle Sebag:
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs. J. Mach. Learn. Res. 23: 219:1-219:62 (2022) - [c149]Herilalaina Rakotoarison, Louisot Milijaona, Andry Rasoanaivo, Michèle Sebag, Marc Schoenauer:
Learning meta-features for AutoML. ICLR 2022 - [c148]Shuyu Dong, Michèle Sebag:
From Graphs to DAGs: A Low-Complexity Model and a Scalable Algorithm. ECML/PKDD (5) 2022: 107-122 - [i39]Shuyu Dong, Michèle Sebag:
From graphs to DAGs: a low-complexity model and a scalable algorithm. CoRR abs/2204.04644 (2022) - [i38]Shuyu Dong, Kento Uemura, Akito Fujii, Shuang Chang, Yusuke Koyanagi, Koji Maruhashi, Michèle Sebag:
High-Dimensional Causal Discovery: Learning from Inverse Covariance via Independence-based Decomposition. CoRR abs/2211.14221 (2022) - 2021
- [j26]Philippe Caillou, Jonas Renault, Jean-Daniel Fekete, Anne-Catherine Letournel, Michèle Sebag:
Cartolabe: A Web-Based Scalable Visualization of Large Document Collections. IEEE Computer Graphics and Applications 41(2): 76-88 (2021) - [c147]Ksenia Gasnikova, Olivier Allais, Michèle Sebag:
Towards causal modeling of nutritional outcomes. CAWS 2021: 5-19 - [c146]Erich Kummerfeld, Thomas Woolf, Will Glad, Michèle Sebag, Sisi Ma:
Important Topics in Causal Analysis: Summary of the CAWS 2021 Round Table Discussion. CAWS 2021: 52-54 - [c145]Omar Shrit, Michèle Sebag:
I2SL: Learn How to Swarm Autonomous Quadrotors Using Iterative Imitation Supervised Learning. EPIA 2021: 418-432 - [c144]Omar Shrit, David Filliat, Michèle Sebag:
Iterative Learning for Model Reactive Control: Application to Autonomous Multi-agent Control. ICARA 2021: 140-146 - [c143]Roman Bresson, Johanne Cohen, Eyke Hüllermeier, Christophe Labreuche, Michèle Sebag:
On the Identifiability of Hierarchical Decision Models. KR 2021: 151-161 - [i37]Victor Berger, Michèle Sebag:
Boltzmann Tuning of Generative Models. CoRR abs/2104.05252 (2021) - [i36]Mikhail Evchenko, Joaquin Vanschoren, Holger H. Hoos, Marc Schoenauer, Michèle Sebag:
Frugal Machine Learning. CoRR abs/2111.03731 (2021) - 2020
- [j25]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, Patrick Loiseau, Clément Moulin-Frier, Santino Nanini, Daniele Quercia, Michèle Sebag, Françoise Fogelman-Soulié, Sofiane Taleb, Liubov Tupikina, Vaibhav Sahu, Jill-Jênn Vie, Fatima Wehbi:
Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities. Frontiers Big Data 3: 577974 (2020) - [c142]Mandar Chandorkar, Cyril Furtlehner, Bala Poduval, Enrico Camporeale, Michèle Sebag:
Dynamic Time Lag Regression: Predicting What & When. ICLR 2020 - [c141]Roman Bresson, Johanne Cohen, Eyke Hüllermeier, Christophe Labreuche, Michèle Sebag:
Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals. IJCAI 2020: 1984-1991 - [i35]Victor Berger, Michèle Sebag:
From abstract items to latent spaces to observed data and back: Compositional Variational Auto-Encoder. CoRR abs/2001.07910 (2020) - [i34]Philippe Caillou, Jonas Renault, Jean-Daniel Fekete, Anne-Catherine Letournel, Michèle Sebag:
Cartolabe: A Web-Based Scalable Visualization of Large Document Collections. CoRR abs/2003.00975 (2020) - [i33]Victor Berger, Michèle Sebag:
Variational Auto-Encoder: not all failures are equal. CoRR abs/2003.01972 (2020) - [i32]Gwendoline de Bie, Herilalaina Rakotoarison, Gabriel Peyré, Michèle Sebag:
Distribution-Based Invariant Deep Networks for Learning Meta-Features. CoRR abs/2006.13708 (2020)
2010 – 2019
- 2019
- [c140]Alice Schoenauer Sebag, Louise Heinrich, Marc Schoenauer, Michèle Sebag, Lani F. Wu, Steven J. Altschuler:
Multi-Domain Adversarial Learning. ICLR (Poster) 2019 - [c139]Herilalaina Rakotoarison, Marc Schoenauer, Michèle Sebag:
Automated Machine Learning with Monte-Carlo Tree Search. IJCAI 2019: 3296-3303 - [c138]Victor Berger, Michèle Sebag:
From Abstract Items to Latent Spaces to Observed Data and Back: Compositional Variational Auto-Encoder. ECML/PKDD (1) 2019: 274-289 - [c137]Guillaume Doquet, Michèle Sebag:
Agnostic Feature Selection. ECML/PKDD (1) 2019: 343-358 - [p6]Olivier Goudet, Diviyan Kalainathan, Michèle Sebag, Isabelle Guyon:
Learning Bivariate Functional Causal Models. Cause Effect Pairs in Machine Learning 2019: 101-153 - [p5]Diviyan Kalainathan, Olivier Goudet, Michèle Sebag, Isabelle Guyon:
Discriminant Learning Machines. Cause Effect Pairs in Machine Learning 2019: 155-189 - [p4]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, Evelyne Viegas:
Analysis of the AutoML Challenge Series 2015-2018. Automated Machine Learning 2019: 177-219 - [i31]Alice Schoenauer Sebag, Louise Heinrich, Marc Schoenauer, Michèle Sebag, Lani F. Wu, Steven J. Altschuler:
Multi-Domain Adversarial Learning. CoRR abs/1903.09239 (2019) - [i30]Herilalaina Rakotoarison, Marc Schoenauer, Michèle Sebag:
Automated Machine Learning with Monte-Carlo Tree Search (Extended Version). CoRR abs/1906.00170 (2019) - [i29]Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales, Wei-Wei Tu, Yang Yu, Lisheng Sun-Hosoya, Isabelle Guyon, Michèle Sebag:
Towards AutoML in the presence of Drift: first results. CoRR abs/1907.10772 (2019) - 2018
- [c136]Lisheng Sun-Hosoya, Isabelle Guyon, Michèle Sebag:
ActivMetal: Algorithm Recommendation with Active Meta Learning. IAL@PKDD/ECML 2018: 48-59 - 2017
- [j24]Mustafa Misir, Michèle Sebag:
Alors: An algorithm recommender system. Artif. Intell. 244: 291-314 (2017) - [j23]Yoann Isaac, Quentin Barthélemy, Cédric Gouy-Pailler, Michèle Sebag, Jamal Atif:
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations. Signal Process. 130: 389-402 (2017) - [c135]Thomas Schmitt, François Gonard, Phillipe Caillou, Michèle Sebag:
Language Modelling for Collaborative Filtering: Application to Job Applicant Matching. ICTAI 2017: 1226-1233 - [r6]Lorenza Saitta, Michèle Sebag:
Grammatical Inference. Encyclopedia of Machine Learning and Data Mining 2017: 569-570 - [r5]Michèle Sebag:
Nonstandard Criteria in Evolutionary Learning. Encyclopedia of Machine Learning and Data Mining 2017: 906-916 - [r4]Lorenza Saitta, Michèle Sebag:
Phase Transitions in Machine Learning. Encyclopedia of Machine Learning and Data Mining 2017: 974-982 - [i28]François Gonard, Marc Schoenauer, Michèle Sebag:
ASAP.V2 and ASAP.V3: Sequential optimization of an Algorithm Selector and a Scheduler. OASC 2017: 8-11 - [i27]Olivier Bousquet, Sylvain Gelly, Karol Kurach, Marc Schoenauer, Michèle Sebag, Olivier Teytaud, Damien Vincent:
Toward Optimal Run Racing: Application to Deep Learning Calibration. CoRR abs/1706.03199 (2017) - [i26]Alice Schoenauer Sebag, Marc Schoenauer, Michèle Sebag:
Stochastic Gradient Descent: Going As Fast As Possible But Not Faster. CoRR abs/1709.01427 (2017) - 2016
- [c134]Thomas Schmitt, Phillipe Caillou, Michèle Sebag:
Matching Jobs and Resumes: a Deep Collaborative Filtering Task. GCAI 2016: 124-137 - [c133]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, Evelyne Viegas:
A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention. AutoML@ICML 2016: 21-30 - [c132]Michèle Sebag, Riad Akrour, Basile Mayeur, Marc Schoenauer:
Anti Imitation-Based Policy Learning. ECML/PKDD (2) 2016: 559-575 - [i25]Yoann Isaac, Quentin Barthélemy, Cédric Gouy-Pailler, Michèle Sebag, Jamal Atif:
Multi-dimensional signal approximation with sparse structured priors using split Bregman iterations. CoRR abs/1609.09525 (2016) - 2015
- [c131]Michèle Sebag:
Collaborative Algorithm Platforms. DATA 2015: IS-5 - [c130]Masaharu Yoshioka, Masahiko Itoh, Michèle Sebag:
Interactive Metric Learning-Based Visual Data Exploration: Application to the Visualization of a Scientific Social Network. ISIP 2015: 142-156 - 2014
- [j22]Michèle Sebag:
A tour of machine learning: An AI perspective. AI Commun. 27(1): 11-23 (2014) - [j21]Cheng-Wei Chou, Ping-Chiang Chou, Jean-Joseph Christophe, Adrien Couëtoux, Pierre de Freminville, Nicolas Galichet, Chang-Shing Lee, Jialin Liu, David Lupien Saint-Pierre, Michèle Sebag, Olivier Teytaud, Mei-Hui Wang, Li-Wen Wu, Shi-Jim Yen:
Strategic Choices in Optimization. J. Inf. Sci. Eng. 30(3): 727-747 (2014) - [j20]Xiangliang Zhang, Cyril Furtlehner, Cécile Germain-Renaud, Michèle Sebag:
Data Stream Clustering With Affinity Propagation. IEEE Trans. Knowl. Data Eng. 26(7): 1644-1656 (2014) - [c129]Joel Ribeiro, Josep Carmona, Mustafa Misir, Michèle Sebag:
A Recommender System for Process Discovery. BPM 2014: 67-83 - [c128]Marc Schoenauer, Riad Akrour, Michèle Sebag, Jean-Christophe Souplet:
Programming by Feedback. ICML 2014: 1503-1511 - [c127]Artémis Llamosi, Adel Mezine, Florence d'Alché-Buc, Véronique Letort, Michèle Sebag:
Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach. ECML/PKDD (2) 2014: 306-321 - [c126]Blaise Hanczar, Michèle Sebag:
Combination of One-Class Support Vector Machines for Classification with Reject Option. ECML/PKDD (1) 2014: 547-562 - [c125]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag, Nikolaus Hansen:
Maximum Likelihood-Based Online Adaptation of Hyper-Parameters in CMA-ES. PPSN 2014: 70-79 - [c124]Guohua Zhang, Michèle Sebag:
Coupling Evolution and Information Theory for Autonomous Robotic Exploration. PPSN 2014: 852-861 - [p3]Sébastien Rebecchi, Hélène Paugam-Moisy, Michèle Sebag:
Learning Sparse Features with an Auto-Associator. Growing Adaptive Machines 2014: 139-158 - [i24]Nicolas Galichet, Michèle Sebag, Olivier Teytaud:
Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits. CoRR abs/1401.1123 (2014) - [i23]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag, Nikolaus Hansen:
Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES. CoRR abs/1406.2623 (2014) - [i22]Luc De Raedt, Siegfried Nijssen, Barry O'Sullivan, Michèle Sebag:
Constraints, Optimization and Data (Dagstuhl Seminar 14411). Dagstuhl Reports 4(10): 1-31 (2014) - 2013
- [j19]Weijia Wang, Michèle Sebag:
Hypervolume indicator and dominance reward based multi-objective Monte-Carlo Tree Search. Mach. Learn. 92(2-3): 403-429 (2013) - [c123]Nicolas Galichet, Michèle Sebag, Olivier Teytaud:
Exploration vs Exploitation vs Safety: Risk-Aware Multi-Armed Bandits. ACML 2013: 245-260 - [c122]Shigeru Takano, Ilya Loshchilov, David Meunier, Michèle Sebag, Einoshin Suzuki:
Fast Adaptive Object Detection towards a Smart Environment by a Mobile Robot. AmI 2013: 182-197 - [c121]Manuel Loth, Michèle Sebag, Youssef Hamadi, Marc Schoenauer:
Bandit-Based Search for Constraint Programming. CP 2013: 464-480 - [c120]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es). GECCO 2013: 439-446 - [c119]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Bi-population CMA-ES agorithms with surrogate models and line searches. GECCO (Companion) 2013: 1177-1184 - [c118]François-Michel De Rainville, Michèle Sebag, Christian Gagné, Marc Schoenauer, Denis Laurendeau:
Sustainable cooperative coevolution with a multi-armed bandit. GECCO 2013: 1517-1524 - [c117]Yoann Isaac, Quentin Barthélemy, Jamal Atif, Cédric Gouy-Pailler, Michèle Sebag:
Multi-dimensional sparse structured signal approximation using split bregman iterations. ICASSP 2013: 3826-3830 - [c116]Rémi Bardenet, Mátyás Brendel, Balázs Kégl, Michèle Sebag:
Collaborative hyperparameter tuning. ICML (2) 2013: 199-207 - [c115]Manuel Loth, Michèle Sebag, Youssef Hamadi, Marc Schoenauer, Christian Schulte:
Hybridizing Constraint Programming and Monte-Carlo Tree Search: Application to the Job Shop Problem. LION 2013: 315-320 - [i21]Yoann Isaac, Quentin Barthélemy, Jamal Atif, Cédric Gouy-Pailler, Michèle Sebag:
Multi-dimensional sparse structured signal approximation using split Bregman iterations. CoRR abs/1303.5197 (2013) - [i20]François-Michel De Rainville, Michèle Sebag, Christian Gagné, Marc Schoenauer, Denis Laurendeau:
Sustainable Cooperative Coevolution with a Multi-Armed Bandit. CoRR abs/1304.3138 (2013) - [i19]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization. CoRR abs/1308.2655 (2013) - 2012
- [j18]Sylvain Gelly, Levente Kocsis, Marc Schoenauer, Michèle Sebag, David Silver, Csaba Szepesvári, Olivier Teytaud:
The grand challenge of computer Go: Monte Carlo tree search and extensions. Commun. ACM 55(3): 106-113 (2012) - [c114]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed. GECCO (Companion) 2012: 175-182 - [c113]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed. GECCO (Companion) 2012: 261-268 - [c112]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Black-box optimization benchmarking of NIPOP-aCMA-ES and NBIPOP-aCMA-ES on the BBOB-2012 noiseless testbed. GECCO (Companion) 2012: 269-276 - [c111]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Self-adaptive surrogate-assisted covariance matrix adaptation evolution strategy. GECCO 2012: 321-328 - [c110]Tianshi Chen, Yunji Chen, Marc Duranton, Qi Guo, Atif Hashmi, Mikko H. Lipasti, Andrew Nere, Shi Qiu, Michèle Sebag, Olivier Temam:
BenchNN: On the broad potential application scope of hardware neural network accelerators. IISWC 2012: 36-45 - [c109]Thomas Philip Runarsson, Marc Schoenauer, Michèle Sebag:
Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling. LION 2012: 160-174 - [c108]Michèle Sebag, Olivier Teytaud:
Upper Confidence Tree-Based Consistent Reactive Planning Application to MineSweeper. LION 2012: 220-234 - [c107]Riad Akrour, Marc Schoenauer, Michèle Sebag:
APRIL: Active Preference Learning-Based Reinforcement Learning. ECML/PKDD (2) 2012: 116-131 - [c106]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Alternative Restart Strategies for CMA-ES. PPSN (1) 2012: 296-305 - [c105]Weijia Wang, Michèle Sebag:
Multi-objective Monte-Carlo Tree Search. ACML 2012: 507-522 - [p2]Jorge Maturana, Álvaro Fialho, Frédéric Saubion, Marc Schoenauer, Frédéric Lardeux, Michèle Sebag:
Adaptive Operator Selection and Management in Evolutionary Algorithms. Autonomous Search 2012: 161-189 - [p1]Alejandro Arbelaez, Youssef Hamadi, Michèle Sebag:
Continuous Search in Constraint Programming. Autonomous Search 2012: 219-243 - [i18]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy. CoRR abs/1204.2356 (2012) - [i17]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed. CoRR abs/1206.0974 (2012) - [i16]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Black-box optimization benchmarking of IPOP-saACM-ES and BIPOP-saACM-ES on the BBOB-2012 noiseless testbed. CoRR abs/1206.5780 (2012) - [i15]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Alternative Restart Strategies for CMA-ES. CoRR abs/1207.0206 (2012) - [i14]Riad Akrour, Marc Schoenauer, Michèle Sebag:
APRIL: Active Preference-learning based Reinforcement Learning. CoRR abs/1208.0984 (2012) - [i13]Thomas Philip Runarsson, Marc Schoenauer, Michèle Sebag:
Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling. CoRR abs/1210.0374 (2012) - 2011
- [j17]Tamás Éltetö, Cécile Germain-Renaud, Pascal Bondon, Michèle Sebag:
Towards Non-Stationary Grid Models. J. Grid Comput. 9(4): 423-440 (2011) - [j16]Cédric Gouy-Pailler, Michèle Sebag, Anthony Larue, Antoine Souloumiac:
Single trial variability in brain-computer interfaces based on motor imagery: Learning in the presence of labeling noise. Int. J. Imaging Syst. Technol. 21(2): 148-157 (2011) - [c104]Cécile Germain-Renaud, Alain Cady, Philippe Gauron, Michel Jouvin, Charles Loomis, Janusz Martyniak, Julien Nauroy, Guillaume Philippon, Michèle Sebag:
The Grid Observatory. CCGRID 2011: 114-123 - [c103]Sylvain Chevallier, Nicolas Bredèche, Hélène Paugam-Moisy, Michèle Sebag:
Emergence of temporal and spatial synchronous behaviors in a foraging swarm. ECAL 2011: 125-132 - [c102]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Not All Parents Are Equal for MO-CMA-ES. EMO 2011: 31-45 - [c101]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
Adaptive coordinate descent. GECCO 2011: 885-892 - [c100]Asuki Kouno, Jean-Marc Montanier, Shigeru Takano, Nicolas Bredèche, Marc Schoenauer, Michèle Sebag, Einoshin Suzuki:
On-Board Evolutionary Algorithm and Off-Line Rule Discovery for Column Formation in Swarm Robotics. IAT 2011: 220-227 - [c99]David Meunier, Michèle Sebag, Shin Ando:
Characterizing Anomalous Behaviors and Revising Robotic Controllers. ICDM Workshops 2011: 705-710 - [c98]Emi Matsumoto, Michèle Sebag, Einoshin Suzuki:
Using SVM to Avoid Humans: A Case of a Small Autonomous Mobile Robot in an Office. ISCIS 2011: 283-287 - [c97]Riad Akrour, Marc Schoenauer, Michèle Sebag:
Preference-Based Policy Learning. ECML/PKDD (1) 2011: 12-27 - [c96]Adrien Couëtoux, Mario Milone, Mátyás Brendel, Hassen Doghmen, Michèle Sebag, Olivier Teytaud:
Continuous RAVE. ACML 2011: 19-31 - 2010
- [j15]Álvaro Fialho, Luís Da Costa, Marc Schoenauer, Michèle Sebag:
Analyzing bandit-based adaptive operator selection mechanisms. Ann. Math. Artif. Intell. 60(1-2): 25-64 (2010) - [j14]José L. Balcázar, Francesco Bonchi, Aristides Gionis, Michèle Sebag:
Guest editors' introduction: special issue of selected papers from ECML PKDD 2010. Data Min. Knowl. Discov. 21(2): 221-223 (2010) - [j13]José L. Balcázar, Francesco Bonchi, Aristides Gionis, Michèle Sebag:
Special issue for ECML PKDD 2010: Guest editors' introduction. Mach. Learn. 81(1): 1-4 (2010) - [c95]Tamás Éltetö, Cécile Germain-Renaud, Pascal Bondon, Michèle Sebag:
Discovering Piecewise Linear Models of Grid Workload. CCGRID 2010: 474-484 - [c94]Ludovic Arnold, Hélène Paugam-Moisy, Michèle Sebag:
Unsupervised Layer-Wise Model Selection in Deep Neural Networks. ECAI 2010: 915-920 - [c93]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
A mono surrogate for multiobjective optimization. GECCO 2010: 471-478 - [c92]Álvaro Fialho, Marc Schoenauer, Michèle Sebag:
Toward comparison-based adaptive operator selection. GECCO 2010: 767-774 - [c91]Álvaro Fialho, Marc Schoenauer, Michèle Sebag:
Fitness-AUC bandit adaptive strategy selection vs. the probability matching one within differential evolution: an empirical comparison on the bbob-2010 noiseless testbed. GECCO (Companion) 2010: 1535-1542 - [c90]Ilya Loshchilov, Marc Schoenauer, Michèle Sebag:
A pareto-compliant surrogate approach for multiobjective optimization. GECCO (Companion) 2010: 1979-1982 - [c89]