Nicolas Vayatis - Publications (by topic)

Predictive modeling: Ranking and scoring


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N. Vayatis
Applications of concentration inequalities for statistical scoring and ranking problems
ESAIM: PROCEEDINGS, Vol. 44, p. 99-109, January 2014.

S. Clémençon, M. Depecker, and N. Vayatis.
Ranking Forests.
(PDF)
Journal of Machine Learning Research.
Volume 14(Jan):39-73, 2013.

S. Clémençon, S. Robbiano, and N. Vayatis.
Ranking data with ordinal labels: optimality and pairwise aggregation.
(PDF)
Machine Learning.
Volume 91(1): 67-104, 2013.

S. Clémençon, M. Depecker, and N. Vayatis.
An empirical comparison of learning algorithms for nonparametric scoring. The TreeRank algorithm and other methods.

Pattern Analysis and Applications. Vol. 16: 475-496, 2013.

S. Clémençon, M. Depecker, and N. Vayatis.
Adaptive partitioning schemes for bipartite ranking.

Machine Learning Journal
. Vol. 83(1): 31-69, 2011.

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, 2011.

S. Clémençon and N. Vayatis.
Overlaying classifiers: A practical approach for optimal scoring.

Constructive Approximation
. Vol. 32(3):619-648, 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, June 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. Paru dans RFIA, Vol.5, n°.3:345-368 (2011).

S. Clémençon, M. Depecker, and N. Vayatis.
Bagging ranking trees.

Proceedings of IEEE-ICMLA'09, pp.658-663, 2009.

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, 2009.

S. Clémençon and N. Vayatis.
Adaptive estimation of the optimal ROC curve and a bipartite ranking algorithm.

Proceedings of ALT'09. Lecture Notes in Computer Science 5809, pp. 216-231, Springer, 2009.

S. Clémençon and N. Vayatis.
On partitioning rules for bipartite ranking.

Proceedings, of AISTATS'09,  Journal of Machine Learning Research, vol.5:89-96, 2009.

S. Clémençon and N. Vayatis.
Nonparametric estimation of the Precision-Recall curve.

Proceedings of ICML'09, L. Bottou and M. Littman (eds), p.185-192, Omnipress, Montreal, 2009.

S. Clémençon and N. Vayatis.
Tree-based ranking methods.

IEEE Transactions on Information Theory. Vol. 55(9):4316-4336, 2009.

S. Clémençon and N. Vayatis.
Empirical performance maximization for linear rank statistics.

Proceedings of NIPS'08, Advances in Neural Information Processing Systems 21, pp. 305-312, MIT Press, 2008.

S. Clémençon and N. Vayatis.
Overlaying classifiers: a practical approach for optimal scoring.

Proceedings of NIPS'08, Advances in Neural Information Processing Systems 21, pp.313-320, MIT Press, 2008.

P. Bertail, S. Clémençon and N. Vayatis.
On bootstrapping the ROC curve.

Proceedings of NIPS'08, Advances in Neural Information Processing Systems 21, pp.137-144, MIT Press, 2008.

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

Proceedings of ALT'08, 2008.

S. Clémençon, G. Lugosi, and N. Vayatis.
Ranking and empirical risk minimization of U-statistics.

The Annals of Statistics
, vol.36(2):844-874, 2008.

S. Clémençon and  N. Vayatis.
Ranking the best instances.

Journal of Machine Learning Research
, 8(Dec):2671-2699, 2007.

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, 2006.

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, 2005.

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, 2005.


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