Publications scientifiques
Published on
Axe 1 : apprentissage par renforcement et optimisation/simulation stochastique
Subsampling for big data : some recent advances. Recent advances in Nonparametric Statistics,
Patrice Bertail, Ons Jelassi, Jessica Tressou-Cosmao, Mélanie Zetlaoui.
Chapitre d'ouvrage - ISNPS 2018, Avignon, Springer Verlag, Berlin, 2018.
Greedy stochastic algorithms for entropy-regularized optimal transport problems. [PDF]
Brahim Khalil Abid and Robert M. Gower.
The AISTATS Conference Proceedings in the Journal of Machine Learning, 2018.
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods. [Link]
Robert M. Gower, Nicolas Le Roux and Francis Bach.
The AISTATS Conference Proceedings in the Journal of Machine Learning, 2018.
A Minimax Optimal Algorithm for Crowdsourcing. [Link]
T. Bonald, R. Combes.
NIPS, 2017.
Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods.
S. Clémençon, F. Portier (Télécom ParisTech).
To appear in the Proceedings of AISTATS 2018, Lanzarote, Spain.
Max-K armed bandit: on the ExtremeHunter algorithm and beyond.
S. Clémençon, M. Achab (Télécom ParisTech), A. Garivier (Université Paul Sabatier), A. Sabourin (Télécom ParisTech) & C. Vernade (Télécom ParisTech).
To appear in the Proceedings of ECML 2017, Skopje, Macedonia.
Sampling and Empirical Risk Minimization
S. Clémençon (Telecom ParisTech), P. Bertail (Université Paris-Ouest) and E. Chautru (Mines ParisTech).
To appear in Statistics, 2017.
Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers
S. Clémençon (Telecom ParisTech), P. Bertail (Université Paris-Ouest) and Guillaume Papa (Telecom ParisTech).
In the Proceedings of ACML 2016, Hamilton (New Zealand).
Empirical processes in survey sampling with (conditional) Poisson designs [Link]
P. Bertail (Université Paris-Ouest), E. Chautru (Mines ParisTech) and S. Clémençon (Telecom ParisTech).
In Scandinavian Journal of Statistics, 2016.
Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics[Link]
S. Clémençon (Telecom ParisTech), A. Bellet (INRIA) and I. Colin (Télécom ParisTech).
In Journal of Machine Learning Research, 2016.
Regularized Cost-Model Oblivious Database Tuning with Reinforcement Learning [PDF]
D. Basu, Q. Lin, W. Chen, H. T. Vo, Z. Yuan, P. Senellart et S. Bressan,
Transactions on Large-Scale Data and Knowledge-Centered Systems, 2016.
Cost-Model Oblivious Database Tuning with Reinforcement Learning [PDF]
D. Basu, Q. Lin, W. Chen, H. T. Vo, Z. Yuan, P. Senellart et S. Bressan,
Proc. DEXA, p. 253‑268, Valence, Espagne, septembre 2015.
Online Influence Maximization [PDF]
S. Lei, S. Maniu, L. Mo, R. Cheng, and P. Senellart
In Proc. KDD, Sydney, Australia, August 2015
Scalable, Generic, and Adaptive Systems for Focused Crawling [PDF]
G. Gouriten, S. Maniu et P. Senellart
Proc. Hypertext, p. 35‑45, Santiago, Chili, septembre 2014.
Prix Douglas Engelbart du meilleur article.
On the complexity of A/B testing [link]
E. Kaufmann, O. Cappé and A. Garivier
In Conference on Learning Theory, Barcelona, Spain, June 2014.
Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach [link]
Artemis Llamosi*, Adel Mezine*, Florence d’Alché-Buc, Véronique Letort, Michèle Sebag.
ECML/PKDD (2) 2014: 306-321 [link]
Crowd Miner: Mining association rules from the crowd [PDF]
Y. Amsterdamer, Y. Grossman, T. Milo et P. Senellart
Proc. VLDB, p. 241‑252, Riva del Garda, Italie, août 2013. Démonstration
Crowd Mining [PDF]
Y. Amsterdamer, Y. Grossman, T. Milo et P. Senellart
Proc. SIGMOD, p. 241‑252, New York, USA, juin 2013.
The KL-UCB algorithm for bounded stochastic bandits and beyond [link]
A. Garivier and O. Cappé.
In Conference on Learning Theory, Budapest, Hungary, July 2011.
Maximal Deviations of Incomplete U-statistics with Applications to Empirical Risk Sampling
S. Clémençon, S. Robbiano (Telecom ParisTech) and J. Tressou (INRA) (2013).
In the Proceedings of the SIAM International Conference on Data-Mining SDM13, Austin (USA).
Scaling-up M-estimation via sampling designs: the Horvitz-Thompson stochastic gradient descent
S. Clémençon, P. Bertail (Université Paris-Ouest) & E. Chautru (Mines ParisTech).
In the Proceedings of the 2014 IEEE International Conference on Big Data, Washington (USA).
Axe 2 : graph-mining et analyse des réseaux sociaux
Are All People Married? Determining Obligatory Attributes in Knowledge Bases. [PDF]
Jonathan Lajus, Fabian M. Suchanek.
Full paper at the Web Conference (WWW) , 2018
A Knowledge Base for Personal Information Management. [PDF]
David Montoya, Thomas Pellissier Tanon, Serge Abiteboul, Fabian M. Suchanek.
Workshop paper at the Linked Data on the Web workshop (LDOW) at WWW , 2018
Adding Missing Words to Regular Expressions. [PDF]
Thomas Rebele, Katerina Tzompanaki, Fabian M. Suchanek.
Full paper at the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) , 2018
A sharp oracle inequality for Graph-Slope. [PDF]
.
Electron. J. Statist., 2017.
A Streaming Algorithm for Graph Clustering. [Link]
A. Hollocou, J. Maudet, T. Bonald, M. Lelarge.
NIPS Workshop, 2017.
Are All People Married? Determining Obligatory Attributes in Knowledge Bases. [PDF]
Jonathan Lajus, Fabian M. Suchanek
Full paper at the Web Conference (WWW), 2018
VICKEY: Mining Conditional Keys on Knowledge Bases [PDF]
Danai Symeonidou, Luis Galárraga, Nathalie Pernelle, Fatiha Saïs, Fabian M. Suchanek
Full paper at International Semantic Web Conference (ISWC) , 2017
Predicting Completeness in Knowledge Bases [PDF]
Luis Galárraga, Simon Razniewski, Antoine Amarilli, Fabian M. Suchanek
Full paper at International Conference on Web Search and Data Mining (WSDM) , December 2016
On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability [Link]
S. Clémençon (Télécom ParisTech) A. Bellet (INRIA), G. Papa (Telecom ParisTech).
In Advances in Neural Information Processing Systems, 2016, Barcelona (Spain).
Ten Years of Knowledge Harvesting: Lessons and Challenges
Gerhard Weikum, Johannes Hoffart, Fabian M. Suchanek
Short journal article in Data Engineering Bulletin, 2016
What if machines could be creative?
Fabian M. Suchanek, Colette Menard, Meghyn Bienvenu, Cyril Chapellier
Demo at International Semantic Web Conference (ISWC), 2016
Thymeflow, a personal knowledge base with spatio-temporal data
David Montoya, Thomas Pellissier Tanon, Serge Abiteboul, Fabian M. Suchanek
Demo at International Conference on Information and Knowledge Management (CIKM), 2016
Can you imagine... a language for combinatorial creativity?
Fabian M. Suchanek, Colette Menard, Meghyn Bienvenu, Cyril Chapellier
Full paper at International Semantic Web Conference (ISWC), 2016
YAGO: a multilingual knowledge base from Wikipedia, Wordnet, and Geonames
Thomas Rebele, Fabian M. Suchanek, Johannes Hoffart, Joanna “Asia” Biega, Erdal Kuzey, Gerhard Weikum:
Short paper at International Semantic Web Conference (ISWC), 2016
Open Digital Forms
Hiep Le, Thomas Rebele, Fabian M. Suchanek
Demo at Theory and Practice of Digital Libraries (TPDL/ECDL), 2016
Input Output Kernel Regression: supervised and semi-supervised structured output prediction with operator-valued kernels
Céline Brouard, Florence d’Alché-Buc, Marie Szafranski
hal-01216708 pdf, accepted at JMLR, September, 2016
Fast metabolite identification with Input Output Kernel Regression
Céline Brouard, Huibin Shen, Kai Dührkop, Florence d’Alché-Buc, Sebastian Böcker, Juho Rousu
ISMB 2016, published in Bioinformatics, May, 2016.
But What Do We Actually Know? [PDF]
Simon Razniewski, Fabian M. Suchanek, Werner Nutt
Workshop paper at Automated Knowledge Base Construction (AKBC), 2016
A Hitchhiker's Guide to Ontology [PDF]
Fabian M. Suchanek
Keynote at French Workshop on the Quality of Data on the Web (QLOD) , 2016
Fast Rule Mining in Ontological Knowledge Bases with AMIE+ PDF
Luis Galárraga, Christina Teflioudi, Katja Hose, Fabian M. Suchanek
Journal article in VLDB Journal(VLDBJ), 2015
See also: AMIE Web page
Adaptive Web Crawling through Structure-Based Link Classification [PDF]
M. Faheem et P. Senellart
Proc. ICADL, Seoul, Corée du Sud, décembre 2015.
Fast Rule Mining in Ontological Knowledge Bases with AMIE+ [PDF]
Luis Galárraga, Christina Teflioudi, Fabian Suchanek, Katja Hose
VLDB Journal, July 2015.
Knowledge Bases for Web Content Analytics [PDF]
Johannes Hoffart, Nicoleta Preda, Fabian M. Suchanek, Gerhard Weikum:
Tutorial at World Wide Web (WWW), 2015
Discovering Meta-Paths in Large Heterogeneous Information Networks [PDF]
C. Meng, R. Cheng, S. Maniu, P. Senellart et W. Zhang
Proc. WWW, Florence, Italie, mai 2015.
A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions [lien]
Adriana Birlutiu, Florence d’Alché-Buc, Tom Heskes.
IEEE Trans. on Computational Biology and Bioinformatics, IEEE Trans. Comp. Biology, issue 99, 5 nov 2014
Efficient Eigen-updating for Spectral Graph Clustering
S. Clémençon, C. Dhanjal (Télécom ParisTech) and R. Gaudel (Lille 3).
Neurocomputing (2014)
Learning Reputation in an Authorship Network
S. Clémençon, C. Dhanjal (Télécom ParisTech).
In the Proceedings of the 29-th ACM Symposium on Applied Computing, Gyeongju (Corée), Mars 2014.
Online Matrix Completion Through Nuclear Norm Regularisation
S. Clémençon, C. Dhanjal (Télécom ParisTech) and R. Gaudel (Lille 3).
In the Proceedings of the SIAM International Conference on Data-Mining SDM14, Philadelphia (USA), 2014.
Learning the Graph of Relations Among Multiple Tasks
S. Clémençon, A. Argyriou (Centrale Paris) and R. Zhang.
In the Proceedings of ICML 2014, Workshop on New Learning Models and Frameworks for Big Data, 2014.
Spontaneous speech and opinion detection: mining call-centre transcripts [lien]
C. Clavel, G. Adda, F. Cailliau, M. Garnier-Rizet, A. Cavet, G. Chapuis, S. Courcinous, C. Danesi, A. Daquo, M. Deldossi, et al.
Language Resources an Evaluation, vol. 47, number 4, pages 1089-1125, Springer, 2013.
Discovering Interesting Information with Advances in Web Technology [PDF]
R. Nayak, P. Senellart, F. M. Suchanek et A. Varde
SIGKDD Explorations, vol. 14, nº 2, p. 63‑81, décembre 2012.
Analyse de forums de discussion pour la relation clients : du text mining au web content mining [lien]
C. Dutrey, A. Peradotto, and C. Clavel.
In Actes de JADT, Liege, 2012.
On maximizing influence [lien]
S. Clémençon, C. Dhanjal (Telecom ParisTech).
In Proceedings of The SIAM Conference on Data Mining 2011, Phoenix (USA).
Hierarchical Clustering for Graph Visualization [lien]
S. Clémençon, H. de Arazoza, V.C. Tran and F. Rossi (2011).
In Proceedings of the European Symposium on Artifical Neural Networks, ESANN 2011 Bruges (Belgium).
Visual Mining of Epidemic Networks [lien]
S. Clémençon, H. de Arazoza, V.C. Tran and F. Rossi (2011).
n Proceedings of the International Work Conference on Artifical Neural Networks, IWANN 2011 Torremolinos (Spain).
Semi-supervised Penalized Output Kernel Regression for Link Prediction [lien]
Brouard, C., d'Alché-Buc, F. and Szafranski, M. (2011)
In Proceedings of the 28th International Conference on Machine Learning (ICML), Bellevue, Washington, USA.
PARIS: Probabilistic Alignment of Relations, Instances, and Schema [PDF]
F. M. Suchanek, S. Abiteboul et P. Senellart
Proceedings of the VLDB Endowment, vol. 5, nº 3, p. 157‑168, décembre 2011.
Extraction probabiliste de chaînes de mots relatives à une opinion [PDF]
R. Lavalley, C. Clavel, and P. Bellot.
Revue Traitement Automatique des Langues, 51 :101–130, 2010.
Corroborating Information from Disagreeing Views [PDF]
A. Galland, S. Abiteboul, A. Marian et P. Senellart
Proc. WSDM, p. 131‑140, New York,
USA, février 2010.
Semantic Culturomics [lien]
Fabian M. Suchanek, Nicoleta Preda
Vision paper at the conference on Very Large Databases (VLDB) [lien]
Voir la présentation | Voir le poster
YAGO3: A Knowledge Base from Multilingual Wikipedias [PDF]
Farzaneh Mahdisoltani, Joanna “Asia” Biega, Fabian M. Suchanek [lien]
Conference on Innovative Data Systems Research (CIDR) [lien]
Voir aussi la page Web de YAGO
Advancing the Utility of Large Knowledge Bases [lien]
Fabian M. Suchanek's French Habilitation thesis (HDR)
Voir la présentation
Knowledge Bases in the Age of Big Data Analytics [lien]
Fabian M. Suchanek, Gerhard Weikum
Tutoriel pour l'International Conference on Very Large Databases (VLDB) [lien]
Voir la page Web du tutoriel
Canonicalizing Open Knowledge Bases [lien]
Luis Galárraga, Kevin P. Murphy, Geremy Heitz, Fabian M. Suchanek
Conference on Information and Knowledge Management (CIKM) [lien]
Recent Research Topics around the YAGO Knowledge Base [lien]
Antoine Amarilli, Luis Galárraga, Nicoleta Preda, Fabian M. Suchanek
invited paper at the Asia Pacific Web Conference (APWEB) [lien]
Axe 3 : ranking et détection d'anomalies
Ranking Median Regression: Learning to Order through Local Consensus.
S. Clémençon, A. Korba (Télécom ParisTech) and E. Sibony.
To appear in the Proceedings of ALT 2018, Lanzarote, Spain.
Ranking Data with Continuous Labels through Oriented Recursive Partitions.
S. Clémençon, M. Achab (Télécom ParisTech).
To appear in the Proceedings of NIPS 2017, Longbeach, USA.
Ranking Median Regression: Learning to Order through Local Consensus.
S. Clémençon, A. Korba (Télécom ParisTech).
To appear in the Proceedings of NIPS 2017, Workshop DISCML, Longbeach, USA.
A Learning Theory of Ranking Aggregation
S. Clémençon, A. Korba & E. Sibony
To appear in the Proceedings of AISTATS 2017, Fort Lauderdale, USA.
Anomaly Detection in Extreme Regions via Empirical MV-sets on the Sphere
S. Clémençon, A. Thomas, A. Sabourin & A. Gramfort.
To appear in the Proceedings of AISTATS 2017, Fort Lauderdale, USA.
Sparse Representation of Multivariate Extremes with Applications to Anomaly Detection
S. Clémençon, N. Goix & A. Sabourin
To appear in Journal of Multivariate Analysis, 2017.
Learning Hyperparameters for Unsupervised Anomaly Detection [Link]
A. Thomas (Telecom ParisTech), S. Clémençon (Telecom ParisTech), A. Gramfort (Telecom ParisTech) and V. Feuillard (Airbus).
In the Proceedings of the 2016 ICML Anomaly Detection Workshop, NYC (USA), Best Paper Award.
Anomaly Detection and Localisation using Mixed Graphical Models [PDF]
R. Laby, F. Roueff, A. Gramfort
Preprint
Controlling the distance to a Kemeny consensus without computing it [PDF]
Yunlong Jiao, Anna Korba, Eric Sibony
ICML 2016
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking [link] [PDF]
Nicolas Goix, Anne Sabourin, Stephan Clémençon
AISTATS 2016, May 2016
Calibration of One-Class SVM for MV set estimation [lien] [PDF]
Albert Thomas, Vincent Feuillard, Alexandre Gramfort
IEEE DSAA, octobre 2015
MRA-based Statistical Learning from Incomplete Rankings
Eric Sibony, Stéphan Clémençon, Jérémie Jakubowicz
In Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France, 2015.
On Anomaly Ranking and Excess-Mass Curves [lien]
Nicolas Goix, Anne Sabourin, Stéphan Clémençon
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, pp. 287–295, 2015
Anomaly Ranking as Supervised Bipartite Ranking
S. Clémençon, S. Robbiano (Télécom ParisTech).
In the Proceedings of the International Conference in Machine Learning ICML’14, Beijing (China), 2014.
Multiresolution analysis of incomplete rankings with applications to prediction
S. Clémençon, Jérémie Jakubowicz (Telecom Sud Paris) & E. Sibony (Telecom ParisTech).
In the Proceedings of the 2014 IEEE Big Data Workshop on Scalable Machine Learning, Washington (USA).
The TreeRank Tournament Algorithm for Multipartite Ranking
S. Clémençon, S. Robbiano (University College of London)
Journal of Nonparametric Statistics, 2014
Extracting Style and Emotion from Handwriting
Laurence Likforman-Sulem, Anna Esposito, Marcos Faundez-Zanuy and Stéphan Clémençon
Actes de WIRN 2014, Vietri-sul-Mare, Mai 2014.
On the Consistency of Ordinal Regression Methods [lien]
Fabian Pedregosa (INRIA Saclay - Ile de France), Francis Bach (INRIA Paris - Rocquencourt, LIENS), Alexandre Gramfort (TSI, LTCI)
En cours d’examen.
On Clustering Rank Data in the Fourier Domain [lien]
S. Clémençon, R. Gaudel (Telecom ParisTech) and J. Jakubowicz (Telecom ParisTech).
In Proceedings of the European Conference in Machine Learning 2011, Athens (Greece).
An Empirical Comparison of Learning Algorithms for Nonparametric Scoring - The TreeRank Algorithm and Other Methods [lien]
S. Clémençon, M. Depecker (Telecom ParisTech) and N. Vayatis (ENSC) (2013).
In Pattern Analysis and its Applications, Vol. 16, No. 4, pp 475-496.
Ranking Forests
S. Clémençon, M. Depecker (Telecom ParisTech) and N. Vayatis (ENSC) (2013).
In Journal of Machine Learning Research, Vol. 14, pp 39-73.
Ranking Data with Ordinal Labels: Optimality and Pairwise Aggregation [lien]
S. Clémençon, S. Robbiano (Telecom ParisTech) and N. Vayatis (ENSC) (2013).
In Machine Learning. Vol. 93, No. 1, pp 67-104.
Scoring anomalies : a M-estimation formulation
S. Clémençon, J. Jakubowicz (Telecom Sud-Management) (2013).
In JMLR W&CP, Vol. 31, Proceedings of AISTATS 2013, Scottsdale (USA).
Axe 4 : cloud learning et algorithmes d'apprentissage distribués
Computational Study of Primitive Emotional Contagion in Dyadic Interaction [lien]
G. Varni, I. Hupont, C. Clavel, M. Chetouani
IEEE Transactions of Affective Computing (early access) (IF = 3.14)
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions
ICML, 2016
Extending Gossip Algorithms to Distributed Estimation of U-Statistics
NIPS, 2015
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning [lien]
Auteurs: A. Bellet, Y. Liang, A. Bagheri Garakani, M.-F. Balcan and F. Sha
SIAM International Conference on Data Mining (SDM), 2015
On-Line Learning Gossip Algorithm in Multi-Agent Systems with Local Decision Rules
S. Clémençon, P. Bianchi (Telecom ParisTech), J. Jakubowicz (Telecom Sud Management) & G. Morral Adel (Telecom ParisTech).
In the Proceedings of the 2013 IEEE International Conference on Big Data, Santa Clara (USA).
Axe 5 : grande dimension - apprentissage et séries/flux de données temporelles
A secure IoT architecture for streaming data analysis and anomaly detection.
Safa Boudabous, Stephan Clémençon, Ons Jelassi and Mariona Caros Roca.
Conférence -IoTSec 2018 Orlando, April, 2018
Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields
Valentin Barriere, Chloé Clavel, Slim Essid
in Proc. ICASSP, Calgary, Apr. 2018 (to appear)
Learning with a Fisher surrogate loss in a small data regime
Moussab Djerrab, Alexandre Garcia, Florence d’Alché-Buc:
Proc. of ESANN (2018)
Output Fisher Embedding Regression
Moussab Djerrab, Alexandre Garcia, Maxime Sangnier, Florence d’Alché-Buc:
Machine Learning Journal, to appear in 2018
Data sparse nonparametric regression with $ε$-insensitive losses
Maxime Sangnier, Olivier Fercoq, Florence d'Alché-Buc:
ACML 2017: 192-207
Parameter estimation of perfusion models in dynamic contrast-enhanced imaging: a unified framework for model comparison
Blandine Romain, Laurence Rouet, Daniel Ohayon, Olivier Lucidarme, Florence d'Alché-Buc, Véronique Letort:
Medical Image Analysis 35: 360-374 (2017)
A Survey on Ensemble Learning for Data Stream Classification. [Link]
Heitor Murilo Gomes, Jean Paul Barddal, Fabrício Enembreck, Albert Bifet:
ACM Comput. Surv. 50(2): 23:1-23:36 (2017)
Adaptive random forests for evolving data stream classification. [Link]
Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfharinger, Geoff Holmes, Talel Abdessalem:
Machine Learning 106(9-10): 1469-1495 (2017)
Classifier Concept Drift Detection and the Illusion of Progress.
Albert Bifet:
ICAISC (2) 2017: 715-725
Droplet Ensemble Learning on Drifting Data Streams.
Pierre-Xavier Loeffel, Albert Bifet, Christophe Marsala, Marcin Detyniecki:
IDA 2017: 210-222
Extremely Fast Decision Tree Mining for Evolving Data Streams.
Albert Bifet, Jiajin Zhang, Wei Fan, Cheng He, Jianfeng Zhang, Jianfeng Qian, Geoff Holmes, Bernhard Pfahringer:
KDD 2017: 1733-1742
A Survey on Ensemble Learning for Data Stream Classification.
Heitor Murilo Gomes, Jean Paul Barddal, Fabrício Enembreck, Albert Bifet:
ACM Comput. Surv. 50(2): 23:1-23:36 (2017)
Transcription of Spanish Historical Handwritten Documents with Deep Neural Networks [Abstract] [PDF]
Emilio Granell , Edgard Chammas, Laurence Likforman-Sulem, Carlos-D. Martínez-Hinarejos, Chafic Mokbel, and Bogdan-Ionut Cîrstea
Journal of Imaging, 2018
Gap Safe screening rules for sparsity enforcing penalties [PDF]
.
JMLR (to appear), 2018.
Heteroscedastic Concomitant Lasso for sparse multimodal electromagnetic brain imaging [PDF]
AISTATS 2018
From safe screening rules to working sets for faster Lasso-type solvers [PDF]
.
NIPS Workshop on Optimization for Machine Learning, 2017
EMOTHAW: A Novel Database for Emotional State Recognition from Handwriting and Drawing.
L. Likforman, A. Espositio, M. Faundes-Zanuy, S. Clémençon & G. Cordasco.
In IEEE Transactions on Human-Machine Systems, November 2016.
Generalization Bounds for Minimum Volume Set Estimation based on Markovian Data.
S. Clémençon, P. Bertail (Université Paris Ouest) and G. Ciolek (Telecom ParisTech).
To appear in the Proceedings of ISAIM 2018, Fort Lauderdale, USA.
Supervised Group Nonnegative Matrix Factorisation with Similarity Constraints and Applications to Speaker Identification
Romain Serizel, Victor Bisot, Slim Essid et Gaël Richard.
In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, 2017.
Feature Learning with Matrix Factorization Applied to Acoustic Scene Classification
Victor Bisot, Romain Serizel, Slim Essid et Gaël Richard.
IEEE Transactions on Audio, Speech, and Language Processing (TASLP), 2017.
Overlapping Sound Event Detection with Supervised Nonnegative Matrix Factorization
Victor Bisot, Slim Essid et Gaël Richard.
In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, 2017.
From safe screening rules to working sets for faster Lasso-type solvers [PDF]
M. Massias, A. Gramfort, J. Salmon, 2017.
On the benefits of output sparsity for multi-label classification [PDF]
E. Chzhen, C. Denis, M. Hebiri, J. Salmon, 2017.
EMOTHAW: A Novel Database for Emotional State Recognition from Handwriting and Drawing
L. Likforman-Sulem, A. Esposito, M. Faundez-Zanuy, S. Clemencon, G. Cordasco
IEEE THMS Transactions on Human-Machine Systems special issue on Drawing and Handwriting Processing for User-Centered Systems, E. Anquetil, G. Pirlo and R. Plamondon Eds (2017)
Gap Safe screening rules for sparsity enforcing penalties [link]
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
arXiv preprint arXiv:1611.05780, November 2016.
Mini-batch stochastic approaches for accelerated multiplicative updates in nonnegative matrix factorisation with beta-divergence [PDF]
Romain Serizel, Slim Essid et Gael Richard.
In Proc. IEEE International Workshop on Machine Learning for Signal Processing (MLSP), September 2016.
Acoustic scene classification with matrix factorization for unsupervised feature learning [PDF]
Victor Bisot, Romain Serizel, Slim Essid et Gael Richard.
In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
Shanghai, China, March 2016.
Group nonnegative matrix factorisation with speaker and session variability compensation for speaker identification [PDF]
Romain Serizel, Slim Essid et Gael Richard
In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
Shanghai, China, March 2016.
Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression [PDF]
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Vincent Leclère, and Joseph Salmon
Preprint
Random Fourier Features For Operator-Valued Kernels
Romina Brault, Florence d'Alché-Buc, Markus Heinonen
ACML 2016
GAP Safe Screening Rules for Sparse-Group Lasso
NIPS, 2016
Parameter estimation of perfusion models in dynamic contrast-enhanced imaging: a unified framework for model comparison
Blandine Romain, Laurence Rouet, Daniel Ohayon, Olivier Lucidarme, Florence d’Alché-Buc, Véronique Letort
Medical Image Analysis Journal, July , 2016.
Joint Quantile Regression in vector-valued RKHSs
Maxime Sangnier, Olivier Fercoq, Florence d’Alché-Buc
Accepted at NIPS 2016.
Scaling up Vector Autoregressive models with operator-valued Random Fourier features
Romain Brault, Néhémy Lim, Florence d’Alché-Buc
AALTD’16, joint workshop to ECML/PKDD 2016.
Early and Reliable Event Detection Using Proximity Space Representation [PDF]
M. Sangnier, J. Gauthier and A. Rakotomamonjy
In Proceedings of the 33rd International Conference on Machine Learning (ICML), New York City, NY, USA, 2016
Handwritten word recognition using Web resources and Recurrent Neural Networks
C. Oprean, L. Likforman-Sulem, A. Popescu, C. Mokbel
International Journal on Document Analysis and Recognition (IJDAR), pp 1-15 (2015)
Time-continuous Estimation of Emotion in Music with Recurrent Neural Networks (regular paper) [PDF]
Thomas Pellegrini, Valentin Barrière
In MediaEval, Wurzen, 14/09/15-15/09/15, CEUR Workshop Proceedings, p. 1-3, septembre 2015
Règles de sélection de variables pour accélerer la localisation de sources en MEG et EEG sous contrainte de parcimonie [PDF]
Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
GRETSI, 2015
Adaptive Multinomial Matrix Completion [PDF]
Olga Klopp (Université paris Ouest), Jean Lafond, Eric Moulines, Joseph Salmon
EJS (to appear).
GAP Safe screening rules for sparse multi-task and multi-class models [lien] [PDF]
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
NIPS 2015
Operator-valued kernel-based vector autoregressive models for network inference [lien]
Néhémy Lim, Florence d’Alché-Buc, Cédric Auliac, George Michailidis
Machine Learning: Volume 99, Issue 3, June 2015
Mind the duality gap: safer rules for the Lasso [lien]
Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
ICML 2015
Probabilistic low-rank matrix completion on finite alphabets [lien]
J. Lafond , O. Klopp (Paris Ouest Nanterre La Défense), É. Moulines , J. Salmon
NIPS 2014
Similarity Learning for High-Dimensional Sparse Data [lien]
Auteurs: K. Liu, A. Bellet and F. Sha
International Conference on Artificial Intelligence and Statistics (AISTATS), 2015
Modelling bid and ask prices using constrained Hawkes processes: Ergodicity and scaling limit [lien]
Ban Zheng, François Roueff, and Frédéric Abergel.
SIAM J. Finan. Math., 5(1): 99–136, February 2014.
Sparse Compositional Metric Learning [lien]
Auteurs: Y. Shi, A. Bellet and F. Sha
AAAI Conference on Artificial Intelligence (AAAI), 2014, 2078-2084
Soft nonnegative matrix co-factorization [lien]
Seichepine, N., Essid, S., Fevotte, C., et Cappe, O.
IEEE Transactions on Signal Processing, PP (99), 2014.
Parametric Estimation of Ordinary Differential Equations with Orthogonality Conditions [lien]
Nicolas J-B. Brunel, Quentin Clairon & Florence d’Alché-Buc.
Journal of American Statistical Association (JASA), Vol. 109(505), pp.173-185, March, (2014).
Autoregressive models for gene regulatory network inference: sparsity, stability and causality issues [lien]
George Michailidis and Florence d'Alché-Buc.
In Special issue on : Parameter estimation in differential equations, Mathematical Biosciences, Springer, Available online, October 28th 2013.
Smooth Nonnegative Matrix Factorization for Unsupervised Audiovisual Document Structuring [lien]
Essid, S. et Fevotte, C.,
IEEE Transactions on Multimedia, 15 (2), pp. 415-425, 2013.
New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks
O. Morillot, L. Likforman-Sulem, E. Grosicki
Journal of Electronic Imaging (JEI), Vol. 22, No 2 (2013)
Learning Optimal Features for Polyphonic Audio-to-Score Alignment [lien]
Joder, C., Essid, S., et Richard, G.,
IEEE Transactions on Audio, Speech, and Language Processing, 21 (10) , pp.2118-2128, 2013.
Adaptive online forecasting of a locally stationary time varying autoregressive process [lien]
C. Giraud, F. Roueff, and A. Sánchez-Pérez
In Statistical Inference for Complex Time Series Data, number 48 in Mathematisches Forschungsinstitut Oberwolfach, pages 53–56, 2013
API design for machine learning software: experiences from the scikit-learn project [lien]
Lars Buitinck (ILPS), Gilles Louppe, Mathieu Blondel, Fabian Pedregosa (INRIA Saclay - Ile de France), Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort (INRIA Saclay - Ile de France, LTCI), Jaques Grobler (INRIA Saclay - Ile de France), Robert Layton, Jake Vanderplas, Arnaud Joly, Brian Holt, Gaël Varoquaux (INRIA Saclay - Ile de France)
European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (2013)
A wavelet-based approach to functional bipartite ranking [lien]
S. Clémençon, M. Depecker (CEA, LIST) (2011).
In Proceedings of IEEE Statistical Signal Processing 2011, Nice (France).
Scikit-learn: Machine Learning in Python [lien]
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay.
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On-line expectation-maximization algorithm for latent data models [lien]
O. Cappé and E. Moulines.
J. Royal Statist. Soc. B, 71(3):593–613, 2009.
Optimal two-step prediction in regression [lien]
Didier Chételat (Cornell), Johannes Lederer (Cornell), Joseph Salmon
Arxiv