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.