Predictive
Modeling: Ranking and scoring
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, 2012.
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.
Learning from Graphs
Kevin
Scaman, Argyris Kalogeratos, Nicolas Vayatis.
What Makes a Good Plan? An
Efficient Planning Approach to Control Diffusion Processes in
Networks.
arXiv:1407.4760, 17 Jul 2014
Rémi
Lemonnier, Kevin Scaman, Nicolas Vayatis.
Tight Bounds for Influence in
Diffusion Networks and Application to Bond Percolation and
Epidemiology.
Proceedings
of NIPS'14
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, 2014.
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, 2014.
E. Richard, P.A. Savalle, and N.
Vayatis.
Estimating simultaneously sparse and
low-rank matrices. (PDF)
Proceedings of ICML'12, 2012
E. Richard, S. Gaiffas, and N.
Vayatis.
Link Prediction in Graphs with
Autoregressive Features. (PDF)
Proceedings of NIPS'12, 2012.
E. Richard,D. Buffoni, N.
Baskiotis, and N. Vayatis.
Taking the best of many link
recommendations and applications to C2C e-commerce.
Preprint, 2012.
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, 2010.
Sequential optimization and
experimental design
E. Contal, and N. Vayatis.
Gaussian Process Optimization with Mutual Information.
arXiv:1311.4825, 2014.
E. Contal, D. Buffoni, A.
Robicquet, and N. Vayatis.
Parallel Gaussian Process Optimization with Upper Confidence Bound
and Pure Exploration . (PDF)(Software).
Proceedings of ECML, Prague, 2013.
Applications to Engineering
and Physics
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,
2014.
S.
Varet, P. Dossantos-Uzarralde, N. Vayatis.
A
statistical approach for experimental cross-section covariances
estimation via shrinkage.
Nuclear
Science
and Engineering, Accepted, 2014.
S.
Varet, P. Dossantos-Uzarralde, N. Vayatis.
Quality
quantification
of evaluated cross section covariances.
Proceedings
of the CW2014 workshop, 2014.
S.
Varet, P. Dossantos-Uzarralde, N. Vayatis.
Uncertainty
estimation of nuclear interaction description from a model
hierarchy.
Proceedings of the
Uncertainties 2014 Conference, 2014.
G. Merle, J.-M. Roussel, V.
Perchet, J.-J. Lesage and N. Vayatis.
Quantitative analysis of Dynamic Fault Trees based on the coupling
of structure functions ure functions and Monte-Carlo simulation.
Quality and Reliability Engineering
International. Accepted, 2014.
J. Costes, J.-M. Ghidaglia, P.
Muguerra, K. L. Nielsen, X. Riou, J.-P. Saut, and N. Vayatis.
On the Simulation of Offshore Oil Facilities at the System Level.
(PDF)
Proceedings of the 10th International Modelica Conference, 2014.
T.S. Stefanakis, E. Contal, N.
Vayatis, F. Dias, and C. E. Synolakis.
Can Small Islands Protect Nearby Coasts From Tsunamis? An Active
Experimental Design Approach .
arXiv:1305.7385.
Proceeding of the Royal Society-A, Accepted, 2014.
F. Dias, S. Guillas, N. Vayatis, A. Sarri, T. S. Stefanakis, E.
Contal and C. E. Synolakis .
New methods for sensitivity analysis and uncertainty
quantification of tsunamis. (PDF)
Proceedings of the 14th Asia
Congress of Fluid Mechanics, Hanoi and Halong, Vietnam.
S. Varet, P. Dossantos-Uzarralde, N. Vayatis, E. Bauge
Pseudo-measurement simulations and shrinkage for the experimental
cross-section covariances optimisation.
Proceedings of the International Conference on Nuclear Data for
Science and Technology, NYC.
P. Dossantos-Uzarralde, N. Vayatis, S. Varet
Statistical selection of numerical models with deterministic
parameters for cross-section uncertainty evaluations.
Proceedings of the International Conference on Nuclear Data for
Science and Technology, NYC.
A. Dematteo, S. Clémençon, N. Vayatis, M. Mougeot.
Sloshing in the LNG shipping industry: risk modelling through
multivariate heavy-tail analysis.
arXiv:1312.0020,
2013.
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, 2012.
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), 2012.
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), 2012.
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, 2011.
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.
Boosting
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.
G. Lugosi and N. Vayatis.
A
consistent strategy for boosting algorithms.
Proceedings
of COLT'2002, University of Sidney, Australia, 2002.
Vapnik-Chervonenkis theory
N. Vayatis.
Exact
Rates in Vapnik-Chervonenkis Bounds.
Annales de l'Institut Henri Poincaré
(B) - Probabilités et Statistiques ,
39(1):95-119, 2003.
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, 2001.
N. Vayatis.
The Role of Critical Sets in
Vapnik-Chervonenkis Theory.
Proceedings of COLT'2000
, Stanford University, 2000.
Mirror descent algorithms
A. Juditsky, A. Nazin, A.
Tsybakov, and N. Vayatis.
Gap-free bounds for stochastic multi-armed bandit.
Proceedings of
IFAC'08 , Seoul, Korea, 2007.
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, 2005.
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, 2005.
Various
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, 2014.
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,
2011.
A. Kohatsu-Higa, N. Vayatis, and K.
Yasuda.
Strong consistency of the Bayesian
estimator for the Ornstein-Uhlenbeck process.
Proceedings of the Metabief Conference, 2011.
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.
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, 2009.