Nicolas Vayatis

ORCID

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 69

Top Venues

Publications

Sylvain W. Combettes, Paul Boniol, Antoine Mazarguil, Danping Wang, Diego Vaquero-Ramos, Marion Chauveau, Laurent Oudre, Nicolas Vayatis, Pierre-Paul Vidal, Alexandra Roren, Marie-Martine Lefèvre-Colau
Arm-CODA: A Data Set of Upper-limb Human Movement During Routine Examination. Image Process. Line 14 (2024)
Alejandro D. de la Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization. AISTATS (2024)
Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
Collaborative non-parametric two-sample testing. CoRR (2024)
Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization. CoRR (2023)
Ioannis Bargiotas, Argyris Kalogeratos, Nicolas Vayatis
A Framework for Paired-Sample Hypothesis Testing for High-Dimensional Data. ICTAI (2023)
Ioannis Bargiotas, Argyris Kalogeratos, Nicolas Vayatis
A framework for paired-sample hypothesis testing for high-dimensional data. CoRR (2023)
Marie Garin, Antoine de Mathelin, Mathilde Mougeot, Nicolas Vayatis
Personalized One-Shot Collaborative Learning. ICTAI (2023)
Charles Truong, Mounir Atiq, Ludovic Minvielle, Renan Serra, Mathilde Mougeot, Nicolas Vayatis
A Data Set for Fall Detection with Smart Floor Sensors. Image Process. Line 13 (2023)
Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification. CoRR (2023)
Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis
Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation. CoRR (2023)
Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
Maximum Weight Entropy. CoRR (2023)
Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis
Collaborative likelihood-ratio estimation over graphs. CoRR (2022)
Pierre Humbert, Laurent Oudre, Nicolas Vayatis, Julien Audiffren
Tensor Convolutional Dictionary Learning With CP Low-Rank Activations. IEEE Trans. Signal Process. 70 (2022)
Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
Fast and Accurate Importance Weighting for Correcting Sample Bias. ECML/PKDD (1) (2022)
Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
Discrepancy-Based Active Learning for Domain Adaptation. ICLR (2022)
Antoine Mazarguil, Laurent Oudre, Nicolas Vayatis
Non-smooth interpolation of graph signals. Signal Process. 196 (2022)
Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
Fast and Accurate Importance Weighting for Correcting Sample Bias. CoRR (2022)
Antoine Mazarguil, Laurent Oudre, Nicolas Vayatis
An Uncertainty Principle for Lowband Graph Signals. IEEE Signal Process. Lett. 29 (2022)
Anne-Flore Baron, Olivier Boulant, Ivan Panico, Nicolas Vayatis
A Compartmental Epidemiological Model Applied to the Covid-19 Epidemic. Image Process. Line 11 (2021)
Antoine de Mathelin, Mathilde Mougeot, Nicolas Vayatis
Discrepancy-Based Active Learning for Domain Adaptation. CoRR (2021)
Pierre Humbert, Batiste Le Bars, Laurent Oudre, Argyris Kalogeratos, Nicolas Vayatis
Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation. J. Mach. Learn. Res. 22 (2021)
Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis
Online non-parametric change-point detection for heterogeneous data streams observed over graph nodes. CoRR (2021)
Amir Dib, Charles Truong, Laurent Oudre, Mathilde Mougeot, Nicolas Vayatis, Heloïse Nonne
Bayesian Feature Discovery for Predictive Maintenance. EUSIPCO (2021)
Antoine de Mathelin, Guillaume Richard, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
Adversarial Weighting for Domain Adaptation in Regression. ICTAI (2021)
Antoine de Mathelin, François Deheeger, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis
ADAPT : Awesome Domain Adaptation Python Toolbox. CoRR (2021)
Sylvain Jung, Laurent Oudre, Charles Truong, Eric Dorveaux, Louis Gorintin, Nicolas Vayatis, Damien Ricard
Adaptive Change-Point Detection for Studying Human Locomotion. EMBC (2021)
Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
Offline detection of change-points in the mean for stationary graph signals. AISTATS (2021)
Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
Offline detection of change-points in the mean for stationary graph signals. CoRR (2020)
Olivier Boulant, Mathilde Fekom, Camille Pouchol, Theodoros Evgeniou, Anton Ovchinnikov, Raphaël Porcher, Nicolas Vayatis
SEAIR Framework Accounting for a Personalized Risk Prediction Score: Application to the Covid-19 Epidemic. Image Process. Line 10 (2020)
Antoine de Mathelin, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis
Adversarial Weighting for Domain Adaptation in Regression. CoRR (2020)
Sylvain Jung, Mona Michaud, Laurent Oudre, Eric Dorveaux, Louis Gorintin, Nicolas Vayatis, Damien Ricard
The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review. Sensors 20 (19) (2020)
Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos
Optimal Multiple Stopping Rule for Warm-Starting Sequential Selection. CoRR (2020)
Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos
Dynamic Epidemic Control via Sequential Resource Allocation. CoRR (2020)
Pierre Humbert, Julien Audiffren, Laurent Oudre, Nicolas Vayatis
Low Rank Activations for Tensor-Based Convolutional Sparse Coding. ICASSP (2020)
Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis
Learning the piece-wise constant graph structure of a varying Ising model. ICML (2020)
Ioannis Bargiotas, Argyris Kalogeratos, Myrto Limnios, Pierre-Paul Vidal, Damien Ricard, Nicolas Vayatis
Multivariate two-sample hypothesis testing through AUC maximization for biomedical applications. SETN (2020)
Pierre Humbert, Laurent Oudre, Nicolas Vayatis, Julien Audiffren
Tensor Convolutional Sparse Coding with Low-Rank activations, an application to EEG analysis. CoRR (2020)
Guillaume Richard, Antoine de Mathelin, Georges Hébrail, Mathilde Mougeot, Nicolas Vayatis
Unsupervised Multi-source Domain Adaptation for Regression. ECML/PKDD (1) (2020)
Charles Truong, Laurent Oudre, Nicolas Vayatis
Selective review of offline change point detection methods. Signal Process. 167 (2020)
A. Mazarguil, Laurent Oudre, Nicolas Vayatis
Localized Interpolation for Graph Signals. EUSIPCO (2020)
Pierre Humbert, Julien Audiffren, Laurent Oudre, Nicolas Vayatis
Multivariate Convolutional Sparse Coding with Low Rank Tensor. CoRR (2019)
Charles Truong, Rémi Barrois-Müller, Thomas Moreau, Clément Provost, Aliénor Vienne-Jumeau, Albane Moreau, Pierre-Paul Vidal, Nicolas Vayatis, Stéphane Buffat, Alain P. Yelnik, Damien Ricard, Laurent Oudre
A Data Set for the Study of Human Locomotion with Inertial Measurements Units. Image Process. Line 9 (2019)
Charles Truong, Laurent Oudre, Nicolas Vayatis
Supervised Kernel Change Point Detection with Partial Annotations. ICASSP (2019)
Clément Dubost, Pierre Humbert, Arno Benizri, Jean-Pierre Tourtier, Nicolas Vayatis, Pierre-Paul Vidal
Selection of the Best Electroencephalogram Channel to Predict the Depth of Anesthesia. Frontiers Comput. Neurosci. 13 (2019)
Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos
Sequential Dynamic Resource Allocation for Epidemic Control. CoRR (2019)
Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos
Sequential Dynamic Resource Allocation for Epidemic Control. CDC (2019)
Charles Truong, Laurent Oudre, Nicolas Vayatis
Greedy Kernel Change-Point Detection. IEEE Trans. Signal Process. 67 (24) (2019)
Pierre Humbert, Laurent Oudre, Nicolas Vayatis
Subsampling of Multivariate Time-Vertex Graph Signals. EUSIPCO (2019)
Ioannis Bargiotas, Julien Audiffren, Nicolas Vayatis, Pierre-Paul Vidal, Alain P. Yelnik, Damien Ricard
Local Assessment of Statokinesigram Dynamics in Time: An in-Depth Look at the Scoring Algorithm. Image Process. Line 9 (2019)
Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos
Optimal Multiple Stopping Rule for Warm-Starting Sequential Selection. ICTAI (2019)
Ioannis Bargiotas, Argyris Kalogeratos, Myrto Limnios, Pierre-Paul Vidal, Damien Ricard, Nicolas Vayatis
Revealing posturographic features associated with the risk of falling in patients with Parkinsonian syndromes via machine learning. CoRR (2019)
Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis
Detecting multiple change-points in the time-varying Ising model. CoRR (2019)
Ioannis Bargiotas, Alice Nicolaï, Pierre-Paul Vidal, Christophe Labourdette, Nicolas Vayatis, Stéphane Buffat
The Complementary Role of Activity Context in the Mental Workload Evaluation of Helicopter Pilots: A Multi-tasking Learning Approach. H-WORKLOAD (2018)
Charles Truong, Laurent Oudre, Nicolas Vayatis
ruptures: change point detection in Python. CoRR (2018)
Laurent Oudre, Rémi Barrois, Thomas Moreau, Charles Truong, Aliénor Vienne-Jumeau, Damien Ricard, Nicolas Vayatis, Pierre-Paul Vidal
Template-Based Step Detection with Inertial Measurement Units. Sensors 18 (11) (2018)
Charles Truong, Laurent Oudre, Nicolas Vayatis
A review of change point detection methods. CoRR (2018)
Thomas Moreau, Laurent Oudre, Nicolas Vayatis
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding. ICML (2018)
Mathilde Fekom, Argyris Kalogeratos, Nicolas Vayatis
The Multi-Round Sequential Selection Problem. CoRR (2018)
Thomas Moreau, Laurent Oudre, Nicolas Vayatis
Distributed Convolutional Sparse Coding. CoRR (2017)
Cédric Malherbe, Nicolas Vayatis
Global optimization of Lipschitz functions. ICML (2017)
Charles Truong, Laurent Oudre, Nicolas Vayatis
Penalty learning for changepoint detection. EUSIPCO (2017)
Ludovic Minvielle, Mounir Atiq, Renan Serra, Mathilde Mougeot, Nicolas Vayatis
Fall detection using smart floor sensor and supervised learning. EMBC (2017)
Kevin Scaman, Argyris Kalogeratos, Luca Corinzia, Nicolas Vayatis
A Spectral Method for Activity Shaping in Continuous-Time Information Cascades. CoRR (2017)
Cédric Malherbe, Emile Contal, Nicolas Vayatis
A ranking approach to global optimization. ICML (2016)
Emile Contal, Nicolas Vayatis
Stochastic Process Bandits: Upper Confidence Bounds Algorithms via Generic Chaining. CoRR (2016)
Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis
Suppressing Epidemics in Networks Using Priority Planning. IEEE Trans. Netw. Sci. Eng. 3 (4) (2016)
Guillaume Merle, Jean-Marc Roussel, Jean-Jacques Lesage, Vianney Perchet, Nicolas Vayatis
Quantitative Analysis of Dynamic Fault Trees Based on the Coupling of Structure Functions and Monte Carlo Simulation. Qual. Reliab. Eng. Int. 32 (1) (2016)
Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks. NIPS (2015)
Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis
A Greedy Approach for Dynamic Control of Diffusion Processes in Networks. ICTAI (2015)
Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann
Guest Editors' foreword. Theor. Comput. Sci. 558 (2014)
Rémi Lemonnier, Nicolas Vayatis
Nonparametric Markovian Learning of Triggering Kernels for Mutually Exciting and Mutually Inhibiting Multivariate Hawkes Processes. ECML/PKDD (2) (2014)
Marc Bonnissel, Joris Costes, Jean-Michel Ghidaglia, Philippe Muguerra, Keld Lund Nielsen, Benjamin Poirson, Xavier Riou, Jean-Philippe Saut, Nicolas Vayatis
A New Framework for the Simulation of Offshore Oil Facilities at the System Level. CSDM (2014)
Emile Contal, Vianney Perchet, Nicolas Vayatis
Gaussian Process Optimization with Mutual Information. ICML (2014)
Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis
What Makes a Good Plan? An Efficient Planning Approach to Control Diffusion Processes in Networks. CoRR (2014)
Emile Richard, Stéphane Gaïffas, Nicolas Vayatis
Link prediction in graphs with autoregressive features. J. Mach. Learn. Res. 15 (1) (2014)
Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. NIPS (2014)
Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. CoRR (2014)
Emile Contal, David Buffoni, Alexandre Robicquet, Nicolas Vayatis
Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration. ECML/PKDD (1) (2013)
Emile Contal, David Buffoni, Alexandre Robicquet, Nicolas Vayatis
Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration CoRR (2013)
Stéphan Clémençon, Sylvain Robbiano, Nicolas Vayatis
Ranking data with ordinal labels: optimality and pairwise aggregation. Mach. Learn. 91 (1) (2013)
Stéphan Clémençon, Marine Depecker, Nicolas Vayatis
An empirical comparison of learning algorithms for nonparametric scoring: the TreeRank algorithm and other methods. Pattern Anal. Appl. 16 (4) (2013)
Emile Contal, Nicolas Vayatis
Gaussian Process Optimization with Mutual Information. CoRR (2013)
Stéphan Clémençon, Marine Depecker, Nicolas Vayatis
Ranking forests. J. Mach. Learn. Res. 14 (1) (2013)
Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann
Editors' Introduction. ALT (2012)
Pierre-André Savalle, Emile Richard, Nicolas Vayatis
Estimation of Simultaneously Sparse and Low Rank Matrices. ICML (2012)
Emile Richard, Stéphane Gaïffas, Nicolas Vayatis
Link Prediction in Graphs with Autoregressive Features. NIPS (2012)
Emile Richard, Andreas Argyriou, Theodoros Evgeniou, Nicolas Vayatis
A Regularization Approach for Prediction of Edges and Node Features in Dynamic Graphs CoRR (2012)
Stéphan Clémençon, Marine Depecker, Nicolas Vayatis
Avancées récentes dans le domaine de l'apprentissage d'ordonnancements. Rev. d'Intelligence Artif. 25 (3) (2011)
Stéphan Clémençon, Marine Depecker, Nicolas Vayatis
Adaptive partitioning schemes for bipartite ranking - How to grow and prune a ranking tree. Mach. Learn. 83 (1) (2011)
Emile Richard, Nicolas Baskiotis, Theodoros Evgeniou, Nicolas Vayatis
Link Discovery using Graph Feature Tracking. NIPS (2010)
Stéphan Clémençon, Nicolas Vayatis
On Partitioning Rules for Bipartite Ranking. AISTATS (2009)
Stéphan Clémençon, Nicolas Vayatis
Adaptive Estimation of the Optimal ROC Curve and a Bipartite Ranking Algorithm. ALT (2009)
Stéphan Clémençon, Nicolas Vayatis
Nonparametric estimation of the precision-recall curve. ICML (2009)
Stéphan Clémençon, Marine Depecker, Nicolas Vayatis
Bagging Ranking Trees. ICMLA (2009)
Odalric-Ambrym Maillard, Nicolas Vayatis
Complexity versus Agreement for Many Views. ALT (2009)
Stéphan Clémençon, Nicolas Vayatis
Tree-based ranking methods. IEEE Trans. Inf. Theory 55 (9) (2009)
Stéphan Clémençon, Nicolas Vayatis, Marine Depecker
AUC optimization and the two-sample problem. NIPS (2009)
Stéphan Clémençon, Nicolas Vayatis
Approximation of the Optimal ROC Curve and a Tree-Based Ranking Algorithm. ALT (2008)
Patrice Bertail, Stéphan Clémençon, Nicolas Vayatis
On Bootstrapping the ROC Curve. NIPS (2008)
Stéphan Clémençon, Nicolas Vayatis
Overlaying classifiers: a practical approach for optimal ranking. NIPS (2008)

Nicolas Vayatis.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Nicolas Vayatis