Nicolas Vayatis - Publications

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Publications by Topic
2016

C. Malherbe, N. Vayatis.
A ranking approach to global optimization.
Journal version. Submitted.

Julien Audiffren, Ioannis Bargiotas, Nicolas Vayatis, Pierre-Paul Vidal, Damien Ricard.
A non linear scoring approach for evaluating balance: classification of elderly as fallers and non-fallers.
Under revision.

R. Barrois-Müller, T. Gregory, L . Oudre, T. Moreau, C. Truong, A. Pulini, S. Buffat, A. Yelnik,, C. de Waele, S. Laporte, N. Vayatis, P.-P. Vidal, D. Ricard, A. Vienne, C. Labourdette.
An automated recording method in clinical consultation to rate the limp in lower limb osteoarthritis.
PLOS One. Accepted.

R. Lemonnier, K. Scaman, N. Vayatis.
Spectral Bounds in Random Graphs Applied to Spreading Phenomena and Percolation.
Submitted.

C. Malherbe, E. Contal, N. Vayatis.
A Ranking Approach to Global Optimization.
Proceedings of ICML'16.

D. Sarkar, E. Contal, N. Vayatis, F. Dias.
Prediction and optimization of wave energy converter arrays using a machine learning approach.
Renewable Energy, Vol.97:504-517.

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 and Monte-Carlo simulation.
Quality and Reliability Engineering International. Volume 32, Issue 1, pages 7-18.

K. Scaman, A. Kalogeratos, N. Vayatis.
Suppressing Epidemics in Networks using Priority-Planning.
IEEE Transactions on Network Science and Engineering. Accepted.



2015

Rémi Barrois, Laurent Oudre, Thomas Moreau, Charles Truong, Nicolas Vayatis, Stéphane Buffat, Alain Yelnik, Catherine de Waele,  Thomas Gregory, Sébastien Laporte, Pierre-Paul Vidal, Damien Ricard.

Quantify osteoarthritis gait at the doctor's office: a simple pelvis accelerometer based method independent from footwear and aging.
Computer Methods in Biomechanics and Biomedical Engineering. Oct;18 Suppl 1:1880-1.

Thomas Durand, Sophie Jacob, Laura Lebouil, Hassen Douzane, Philippe Lestaevel, Amithys Rahimian, Dimitri Psimaras, Loic Feuvret, Delphine Leclercq, Bruno Brochet, Radia Tamarat, Fabien Millat, Marc Benderitter, George Noel, Nicolas Vayatis, Khe Hoang-Xuan, Jean-Yves Delattre, Damien Ricard, Marie-Odile Bernier.
EpiBrainRad: an epidemiologic study of the neurotoxicity induced by radiotherapy in high grade glioma patients.
BMC Neurology, Accepted.

Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis.
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks.
Proceedings of NIPS'15.

Kevin Scaman, Argyris Kalogeratos, and Nicolas Vayatis.
Learning to Suppress SIS Epidemics in Networks.
NIPS workshop “Networks in the Social and Information Sciences”, NIPS'15.

Emile Contal, Cédric Malherbe, Nicolas Vayatis.
Optimization for Gaussian Processes via Chaining.
NIPS workshop “Bayesian Optimization: Scalability and Flexibility”, NIPS'15.

Thomas Moreau, Laurent Oudre, Nicolas Vayatis.
Distributed Convolutional Sparse Coding via Message Passing Interface.
NIPS workshop “
Nonparametric Methods for Large Scale Representation Learning”, NIPS'15.

Kevin Scaman, Argyris Kalogeratos, and Nicolas Vayatis.
A Greedy Approach for Dynamic Control of Diffusion Processes in Networks.
Proceedings of the IEEE International Conference on Tools with Artificial Intelligence (ICTAI), November 9-12, 2015.

Suzanne Varet, Pierre Dossantos-Uzarralde, Nicolas Vayatis.
Quality quantification of evaluated cross section covariances.
Nuclear Data Sheets 123, p.191-195, (2015).

Suzanne Varet, Pierre Dossantos-Uzarralde, Nicolas Vayatis.
A statistical approach for experimental crosssection covariances estimation via shrinkage.
Nuclear Science and Engineering 179(4), p.398-410 (2015).

Thomas Moreau , Laurent Oudre , Nicolas Vayatis.
Groupement automatique pour l'analyse du spectre singulier.
Colloque du GRETSI.

Charles Truong , Laurent Oudre , Nicolas Vayatis.
Segmentation de signaux physiologiques par optimisation globale.
Colloque du GRETSI.

Dripta Sarkar, Emile Contal, Nicolas Vayatis, Frederic Dias.
A Machine Learning Approach to the Analysis of Wave Energy Converters.
Proceedings of OMAE 2015.

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. Accepted. [
arXiv:1305.7385.]

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.

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

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.

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

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, P.A. Savalle, and N. Vayatis.
Estimating simultaneously sparse and low-rank matrices.
  (PDF)
Proceedings of ICML'12.

E. Richard, S. Gaiffas, and N. Vayatis.
Link Prediction in Graphs with Autoregressive Features.
(PDF)
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.


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