2012

E. Richard, P.A. Savalle, and N. Vayatis.
Estimating simultaneously sparse and low-rank matrices.
Proceedings of ICML'12. To appear.

E. Richard, P.A. Savalle, and N. Vayatis.
Graph Prediction in a Low-Rank and Autoregressive Setting.
Submitted.

E. Richard,D. Buffoni, N. Baskiotis, and N. Vayatis.
Taking the best of many link recommendations and applications to C2C e-commerce.
Submitted.

S. Clémençon, S. Robbiano, and N. Vayatis.
Ranking Ordinal Data: Optimality and Pairwise Aggregation.
Under revision.

T.S. Stefanakis, F. Dias, N. Vayatis, and S. Guillas.
Long-Wave Runup On A Plane Beach Behind A Conical Island.
Proceedings of 15 WCEE, Lisboa. To appear.

 

2011

S. Clémençon, M. Depecker, and N. Vayatis.
Ranking Forests.
Under revision.

S. Clémençon, M. Depecker, and N. Vayatis.
Adaptive partitioning schemes for bipartite ranking. (PDF)
Machine Learning Journal. Vol. 83(1): 31-69.

S. Varet, P. Dossantos-Uzarralde, N. Vayatis, R. Brault, and E. Bauge.
Experimental Covariances Contributions to Evaluated Cross Section Uncertainty Determination.
Proceedings of the Second Workshop on Neutron Cross Section Covariances, Vienna.

A. Kohatsu, N. Vayatis, and K. Yasuda.
Strong consistency of Bayesian estimator under discrete observations and unknown transition density.
in Stochastic Analysis with Financial Applications: Hong Kong 2009, A. Kohatsu-Higa, N. Privault, S.-J. Sheu eds., Birkhauser, pp. 145-168.

A. Kohatsu-Higa, N. Vayatis, and K. Yasuda.
Strong consistency of the Bayesian estimator for the Ornstein-Uhlenbeck process.
Proceedings of the Metabief Conference.

S. Clémençon, M. Depecker, and N. Vayatis.
Nonparametric scoring methods as a support decision tool for medical diagnosis.
Proceedings of the Workshop on Knowledge Discovery in Health Care and Medicine at ECML-KDD'2011.

 

2010

E. Richard, N. Baskiotis, T. Evgeniou, and N. Vayatis.
Link discovery using Graph Feature Tracking.
Proceedings of NIPS'10, Advances in Neural Information Processing Systems 23, MIT Press.

S. Clémençon and N. Vayatis.
Overlaying classifiers: A practical approach for optimal scoring. (PDF)
Constructive Approximation. Vol. 32(3):619-648.

G. Merle, J.M. Roussel, J.J Lesage, and N. Vayatis.
Analytical Calculation of Failure Probabilities in Dynamic Default Trees including Spare Gates.
Proceedings of ESREL 2010 - European Safety a& Reliability Conference. September 2010.

J. Defretin, S. Herbin, G. Le Besnerais, and N. Vayatis.
Adaptive Planification in Active 3D Object Recognition for Many Classes of Objects.
RSS 2010 Workshop - Robotics, Science and Systems. June 2010.

N. Baskiotis, S. Clémençon, M. Depecker, and N. Vayatis.
TreeRank: an R package for bipartite ranking.
Proceedings of SMDTA 2010 - Stochastic Modeling Techniques and Data Analysis International Conference. Juin 2010.

S. Clémençon, M. Depecker, and N. Vayatis.
Données avec label binaire : avancées récentes dans le domaine de l'apprentissage statisticque d'ordonnancements.
CAP 2010 - conférence Francophone sur l'Apprentissage Automatique. Mai 2010.
Prix du meilleur article de la conférence. A paraître dans RFIA.

 

2009

S. Clémençon, M. Depecker, and N. Vayatis.
Bagging ranking trees.
Proceedings of IEEE-ICMLA'09, pp.658-663.

S. Clémençon, M. Depecker, and N. Vayatis.
AUC maximization and the two-sample problem.
Proceedings of NIPS'09, Advances in Neural Information Processing Systems 22, pp.360-368, MIT Press.

S. Clémençon and N. Vayatis.
Adaptive estimation of the optimal ROC curve and a bipartite ranking algorithm. (PDF)
Proceedings of ALT'09. Lecture Notes in Computer Science 5809, pp. 216-231, Springer.

O. Ambrym-Maillard and N. Vayatis.
Complexity versus agreement for many views. (PDF)
Proceedings of ALT'09, Lecture Notes in Computer Science, pp. 232-246, Springer.

S. Clémençon and N. Vayatis.
On partitioning rules for bipartite ranking. (PDF)
Proceedings, of AISTATS'09,  Journal of Machine Learning Research, vol.5:89-96.

S. Clémençon and N. Vayatis.
Nonparametric estimation of the Precision-Recall curve. (PDF)
Proceedings of ICML'09, L. Bottou and M. Littman (eds), p.185-192, Omnipress, Montreal.

S. Clémençon and N. Vayatis.
Tree-based ranking methods. (PDF)
IEEE Transactions on Information Theory. Vol. 55(9):4316-4336.

 

2008

S. Clémençon and N. Vayatis.
Empirical performance maximization for linear rank statistics. (PDF)
Proceedings of NIPS'08, Advances in Neural Information Processing Systems 21, pp. 305-312, MIT Press.

S. Clémençon and N. Vayatis.
Overlaying classifiers: a practical approach for optimal scoring. (PDF)
Proceedings of NIPS'08, Advances in Neural Information Processing Systems 21, pp.313-320, MIT Press.

P. Bertail, S. Clémençon and N. Vayatis.
On bootstrapping the ROC curve. (PDF)
Proceedings of NIPS'08, Advances in Neural Information Processing Systems 21, pp.137-144, MIT Press.

S. Clémençon and N. Vayatis.
Tree-structured ranking rules and approximation of the optimal ROC curve.
Proceedings of ALT'08.

S. Clémençon, G. Lugosi, and N. Vayatis.
Ranking and empirical risk minimization of U-statistics. (PDF)
The Annals of Statistics, vol.36(2):844-874.

 

2007

A. Juditsky, A. Nazin, A. Tsybakov, and N. Vayatis.
Gap-free bounds for stochastic multi-armed bandit. (PDF)
Proceedings of IFAC'08, Seoul, Korea.

S. Clémençon and  N. Vayatis.
Ranking the best instances. (PDF)  
Journal of Machine Learning Research, 8(Dec):2671-2699.

 

2006

S. Clémençon, G. Lugosi, and N. Vayatis.
Discussion on the 2004 IMS Medallion Lecture "Local Rademacher complexities and oracle inequalities in risk minimization" by V. Koltchinskii.
The  Annals of Statistics, 34(6):2672-2676.

N. Vayatis.
Habilitation thesis. (PDF)
Université Paris 6.

 

2005

A. Juditsky, A. Nazin, A. Tsybakov, and N. Vayatis.
Recursive Aggregation of Estimators via the  Mirror Descent Algorithm with averaging. (PDF)
Problems of Information Transmission,  41(4): 368-384.

S. Clémençon, G. Lugosi, and N.Vayatis.
Ranking and scoring using empirical risk minimization.
Proceedings of COLT 2005, in LNCS Computational Learning Theory, vol. 3559, pp.1--15, Springer.

S. Clémençon, G. Lugosi, and N.Vayatis.
From Ranking to Classification: a Statistical View.
Proceedings of the 29th Annual Conference of the German Classification Society (GfKl 2005), University of Magdeburg.

A. Juditsky, A. Nazin, A. Tsybakov and N. Vayatis.
Generalization Error Bounds for Aggregation by Mirror Descent With Averaging.
Proceedings of Neural Information Processing Systems NIPS'2005, MIT Press.

 

2004

G. Lugosi and N. Vayatis.
On the Bayes-risk consistency of regularized boosting methods (with discussion). (PDF)
The Annals of Statistics, 32(1):30-55.

G. Lugosi and N. Vayatis.
Rejoinder on Three Boosting Papers.
The Annals of Statistics, 32(1):124-127.

 

2003

G. Blanchard, G. Lugosi and N. Vayatis.
On the rate of convergence of regularized boosting methods. (PDF)
Journal of Machine Learning Research, 4(Oct):861-894.

N. Vayatis.
Exact Rates in Vapnik-Chervonenkis Bounds. (PS).
Annales de l'Institut Henri Poincaré (B) - Probabilités et Statistiques, 39(1):95-119.

2002

G. Lugosi and N. Vayatis.
A consistent strategy for boosting algorithms.
Proceedings of COLT'2002, University of Sidney, Australia.

2001

R. Azencott and N. Vayatis.
Refined Exponential Rates in Vapnik-Chervonenkis Inequalities.
Comptes Rendus de l'Académie des Sciences de Paris, t.332, série I, p.563-568.

2000

N. Vayatis.
The Role of Critical Sets in Vapnik-Chervonenkis Theory. (PDF)
Proceedings of COLT'2000, Stanford University. 

N. Vayatis.
PhD thesis. (PDF)
Ecole Polytechnique.

 

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