2014
Themistoklis S. Stefanakis, Emile Contal,
Nicolas Vayatis, Frédéric Dias, and Costas E. Synolakis.
Can Small Islands Protect Nearby Coasts From Tsunamis? An
Active Experimental Design Approach.
Proceedings of the Royal Society-A. November.
Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis.
Tight Bounds for Influence
in Diffusion Networks and Application to Bond Percolation
and Epidemiology.
Proceedings
of NIPS'14
Rémi Lemonnier, Nicolas Vayatis.
Nonparametric Markovian Learning
of Triggering Kernels for Mutually Exciting and Mutually
Inhibiting Multivariate Hawkes Processes.
Proceedings
of ECML'14
Emile Contal, Vianney Perchet, Nicolas Vayatis.
Gaussian Process Optimization
with Mutual Information.
Proceedings
of ICML'14 and JMLR W&CP 32 (1) : 253–261
Joris Costes, Jean-Michel Ghidaglia, Philippe
Muguerra, Keld Lund Nielsen, Xavier Riou, Jean-Philippe Saut
and Nicolas Vayatis.
On the Simulation of Offshore Oil Facilities at the System
Level. (PDF)
Proceedings of the 10th International Modelica Conference
K. Scaman, A.
Kalogeratos, N. Vayatis.
Dynamic Treatment Allocation for Epidemic Control in
Arbitrary Networks. (PDF)
Proceedings of WSDM 2014 Diffusion in Networks and Cascade
Analytics (DiffNet) Workshop, February, NYC.
E. Richard, S. Gaiffas,
and N. Vayatis.
Link Prediction in Graphs
with Autoregressive Features. (PDF)
Journal of Machine Learning Research. Volume 15(Feb):565−593.
S. Varet, P. Dossantos-Uzarralde, N. Vayatis and
E. Bauge.
A method using
Pseudo-measurements and shrinkage for the estimation of
cross section covariances.
Nuclear Data Sheets, 118,
p.357-359.
S. Varet, P. Dossantos-Uzarralde, N. Vayatis.
A statistical approach for
experimental cross-section covariances estimation via
shrinkage.
Nuclear
Science and Engineering, Accepted.
S. Varet, P. Dossantos-Uzarralde, N. Vayatis.
Quality quantification of
evaluated cross section covariances.
Proceedings of the CW2014
workshop.
S. Varet, P. Dossantos-Uzarralde, N. Vayatis.
Uncertainty estimation of
nuclear interaction description from a model hierarchy.
Proceedings of the
Uncertainties 2014 Conference.
A. Kohatsu-Higa, N.
Vayatis, and K. Yasuda
Strong Consistency of the Bayesian Estimator for the
Ornstein–Uhlenbeck Process.
Book Chapter in Y. Kabanov, M. Rutkowski, T. Zariphopoulou
(eds.), Inspired by Finance - The Musiela Festschrift:
411-437.
2013
S. Clémençon, M.
Depecker, and N. Vayatis.
Ranking Forests.
(PDF)
Journal of Machine Learning Research. Volume 14(Jan):39-73.
S. Clémençon, S.
Robbiano, and N. Vayatis.
Ranking data with ordinal
labels: optimality and pairwise aggregation.
(PDF)
Machine Learning. Volume 91(1): 67-104.
E. Richard, A. Argyriou,
T. Evgeniou, N. Vayatis.
A Regularization Approach for Prediction of Edges and Node
Features in Dynamic Graphs.
arXiv:1203.5438
Emile Contal, David
Buffoni, Alexandre Robicquet, and N. Vayatis.
Parallel Gaussian Process Optimization with Upper
Confidence Bound and Pure Exploration . (PDF)(Software).
Proceedings of European Conference on Machine Learning,
Prague.
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.
2012
E. Richard, S. Gaiffas, and
N. Vayatis.
Link Prediction in Graphs with
Autoregressive Features.
Proceedings of NIPS'12.
E. Richard,D. Buffoni, N.
Baskiotis, and N. Vayatis.
Taking the best of many link
recommendations and applications to C2C e-commerce.
Preprint.
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.
S. Varet, P.
Dossantos-Uzarralde, N. Vayatis, and E. Bauge.
Pseudo-measurement
simulations and bootstrap for the experimental cross-section
covariances estimation with quality qualification.
Wonder 2012: 3rd International Workshop on Nuclear Data
Evaluation for Reactor Applications (Aix-en-Provence).
S. Varet, A. Garlaud, P.
Dossantos-Uzarralde, N. Vayatis, and E. Bauge.
Kriging approach for the experimental cross-section
covariances estimation.
Wonder 2012: 3rd International Workshop on Nuclear Data
Evaluation for Reactor Applications (Aix-en-Provence).
2011
S. Clémençon, M. Depecker, and
N. Vayatis.
Adaptive partitioning schemes for bipartite ranking.
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.
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. Paru dans RFIA,
Vol.5, n°.3:345-368 (2011).
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.
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.
Proceedings of ALT'09, Lecture Notes in Computer Science, pp.
232-246, Springer.
S. Clémençon and N.
Vayatis.
On partitioning rules for
bipartite ranking.
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.
Proceedings of ICML'09, L. Bottou and M. Littman (eds),
p.185-192, Omnipress, Montreal.
S. Clémençon and N.
Vayatis.
Tree-based ranking methods.
IEEE Transactions on Information Theory. Vol. 55(9):4316-4336.
2008
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.
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.
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.
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.
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.
Proceedings of
IFAC'08 , Seoul, Korea.
S. Clémençon and N. Vayatis.
Ranking the best instances.
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.
Université Paris 6.
2005
A. Juditsky, A. Nazin, A.
Tsybakov, and N. Vayatis.
Recursive Aggregation of
Estimators via the Mirror Descent Algorithm with averaging.
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).
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.
Journal of Machine Learning
Research , 4(Oct):861-894.
N. Vayatis.
Exact Rates in Vapnik-Chervonenkis Bounds.
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.
Proceedings of COLT'2000
, Stanford University.
N. Vayatis.
PhD thesis.
Ecole Polytechnique.