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Nicolas Vayatis
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2020 – today
- 2024
- [j29]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: 1-13 (2024) - [c51]Alejandro D. de la Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos:
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization. AISTATS 2024: 1189-1197 - [i31]Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos:
Collaborative non-parametric two-sample testing. CoRR abs/2402.05715 (2024) - 2023
- [j28]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: 183-197 (2023) - [c50]Ioannis Bargiotas, Argyris Kalogeratos, Nicolas Vayatis:
A Framework for Paired-Sample Hypothesis Testing for High-Dimensional Data. ICTAI 2023: 16-21 - [c49]Marie Garin, Antoine de Mathelin, Mathilde Mougeot, Nicolas Vayatis:
Personalized One-Shot Collaborative Learning. ICTAI 2023: 114-121 - [i30]Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis:
Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation. CoRR abs/2301.03011 (2023) - [i29]Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis:
Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification. CoRR abs/2304.04042 (2023) - [i28]Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis:
Maximum Weight Entropy. CoRR abs/2309.15704 (2023) - [i27]Ioannis Bargiotas, Argyris Kalogeratos, Nicolas Vayatis:
A framework for paired-sample hypothesis testing for high-dimensional data. CoRR abs/2309.16274 (2023) - [i26]Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos:
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization. CoRR abs/2311.01900 (2023) - 2022
- [j27]Antoine Mazarguil, Laurent Oudre
, Nicolas Vayatis:
Non-smooth interpolation of graph signals. Signal Process. 196: 108480 (2022) - [j26]Antoine Mazarguil
, Laurent Oudre
, Nicolas Vayatis
:
An Uncertainty Principle for Lowband Graph Signals. IEEE Signal Process. Lett. 29: 727-731 (2022) - [j25]Pierre Humbert
, Laurent Oudre
, Nicolas Vayatis, Julien Audiffren
:
Tensor Convolutional Dictionary Learning With CP Low-Rank Activations. IEEE Trans. Signal Process. 70: 785-796 (2022) - [c48]Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis:
Discrepancy-Based Active Learning for Domain Adaptation. ICLR 2022 - [c47]Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis:
Fast and Accurate Importance Weighting for Correcting Sample Bias. ECML/PKDD (1) 2022: 659-674 - [i25]Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis:
Collaborative likelihood-ratio estimation over graphs. CoRR abs/2205.14461 (2022) - [i24]Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis:
Fast and Accurate Importance Weighting for Correcting Sample Bias. CoRR abs/2209.04215 (2022) - 2021
- [j24]Anne-Flore Baron, Olivier Boulant
, Ivan Panico, Nicolas Vayatis:
A Compartmental Epidemiological Model Applied to the Covid-19 Epidemic. Image Process. Line 11: 105-119 (2021) - [j23]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: 195:1-195:47 (2021) - [c46]Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos:
Offline detection of change-points in the mean for stationary graph signals. AISTATS 2021: 3430-3438 - [c45]Sylvain Jung, Laurent Oudre, Charles Truong, Eric Dorveaux, Louis Gorintin, Nicolas Vayatis, Damien Ricard:
Adaptive Change-Point Detection for Studying Human Locomotion. EMBC 2021: 2020-2024 - [c44]Amir Dib, Charles Truong, Laurent Oudre
, Mathilde Mougeot, Nicolas Vayatis, Heloïse Nonne:
Bayesian Feature Discovery for Predictive Maintenance. EUSIPCO 2021: 1421-1425 - [c43]Antoine de Mathelin, Guillaume Richard, François Deheeger, Mathilde Mougeot, Nicolas Vayatis:
Adversarial Weighting for Domain Adaptation in Regression. ICTAI 2021: 49-56 - [i23]Antoine de Mathelin, Mathilde Mougeot, Nicolas Vayatis:
Discrepancy-Based Active Learning for Domain Adaptation. CoRR abs/2103.03757 (2021) - [i22]Antoine de Mathelin, François Deheeger, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis:
ADAPT : Awesome Domain Adaptation Python Toolbox. CoRR abs/2107.03049 (2021) - [i21]Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis:
Online non-parametric change-point detection for heterogeneous data streams observed over graph nodes. CoRR abs/2110.10518 (2021) - 2020
- [j22]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: 150-166 (2020) - [j21]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): 5625 (2020) - [j20]Charles Truong
, Laurent Oudre
, Nicolas Vayatis:
Selective review of offline change point detection methods. Signal Process. 167 (2020) - [c42]Antoine Mazarguil, Laurent Oudre
, Nicolas Vayatis:
Localized Interpolation for Graph Signals. EUSIPCO 2020: 2160-2164 - [c41]Pierre Humbert, Julien Audiffren
, Laurent Oudre
, Nicolas Vayatis:
Low Rank Activations for Tensor-Based Convolutional Sparse Coding. ICASSP 2020: 3252-3256 - [c40]Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis:
Learning the piece-wise constant graph structure of a varying Ising model. ICML 2020: 675-684 - [c39]Guillaume Richard, Antoine de Mathelin, Georges Hébrail, Mathilde Mougeot, Nicolas Vayatis:
Unsupervised Multi-source Domain Adaptation for Regression. ECML/PKDD (1) 2020: 395-411 - [c38]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: 56-59 - [i20]Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos:
Optimal Multiple Stopping Rule for Warm-Starting Sequential Selection. CoRR abs/2002.05160 (2020) - [i19]Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos:
Dynamic Epidemic Control via Sequential Resource Allocation. CoRR abs/2006.07199 (2020) - [i18]Antoine de Mathelin, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis:
Adversarial Weighting for Domain Adaptation in Regression. CoRR abs/2006.08251 (2020) - [i17]Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos:
Offline detection of change-points in the mean for stationary graph signals. CoRR abs/2006.10628 (2020) - [i16]Pierre Humbert, Laurent Oudre, Nicolas Vayatis, Julien Audiffren:
Tensor Convolutional Sparse Coding with Low-Rank activations, an application to EEG analysis. CoRR abs/2007.02534 (2020)
2010 – 2019
- 2019
- [j19]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: 65 (2019) - [j18]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: 143-157 (2019) - [j17]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: 381-390 (2019) - [j16]Charles Truong
, Laurent Oudre
, Nicolas Vayatis:
Greedy Kernel Change-Point Detection. IEEE Trans. Signal Process. 67(24): 6204-6214 (2019) - [c37]Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos:
Sequential Dynamic Resource Allocation for Epidemic Control. CDC 2019: 6338-6343 - [c36]Pierre Humbert, Laurent Oudre
, Nicolas Vayatis:
Subsampling of Multivariate Time-Vertex Graph Signals. EUSIPCO 2019: 1-5 - [c35]Charles Truong
, Laurent Oudre
, Nicolas Vayatis:
Supervised Kernel Change Point Detection with Partial Annotations. ICASSP 2019: 3147-3151 - [c34]Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos:
Optimal Multiple Stopping Rule for Warm-Starting Sequential Selection. ICTAI 2019: 1429-1433 - [i15]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 abs/1907.06614 (2019) - [i14]Pierre Humbert, Julien Audiffren, Laurent Oudre, Nicolas Vayatis:
Multivariate Convolutional Sparse Coding with Low Rank Tensor. CoRR abs/1908.03367 (2019) - [i13]Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos:
Sequential Dynamic Resource Allocation for Epidemic Control. CoRR abs/1909.09678 (2019) - [i12]Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis:
Detecting multiple change-points in the time-varying Ising model. CoRR abs/1910.08512 (2019) - 2018
- [j15]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): 4033 (2018) - [c33]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: 222-238 - [c32]Thomas Moreau, Laurent Oudre
, Nicolas Vayatis:
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding. ICML 2018: 3623-3631 - [i11]Charles Truong, Laurent Oudre, Nicolas Vayatis:
A review of change point detection methods. CoRR abs/1801.00718 (2018) - [i10]Charles Truong, Laurent Oudre, Nicolas Vayatis:
ruptures: change point detection in Python. CoRR abs/1801.00826 (2018) - [i9]Mathilde Fekom, Argyris Kalogeratos, Nicolas Vayatis:
The Multi-Round Sequential Selection Problem. CoRR abs/1809.07299 (2018) - 2017
- [c31]Ludovic Minvielle, Mounir Atiq, Renan Serra, Mathilde Mougeot, Nicolas Vayatis:
Fall detection using smart floor sensor and supervised learning. EMBC 2017: 3445-3448 - [c30]Charles Truong
, Laurent Oudre, Nicolas Vayatis:
Penalty learning for changepoint detection. EUSIPCO 2017: 1569-1573 - [c29]Cédric Malherbe, Nicolas Vayatis:
Global optimization of Lipschitz functions. ICML 2017: 2314-2323 - [i8]Thomas Moreau, Laurent Oudre, Nicolas Vayatis:
Distributed Convolutional Sparse Coding. CoRR abs/1705.10087 (2017) - [i7]Kevin Scaman, Argyris Kalogeratos, Luca Corinzia, Nicolas Vayatis:
A Spectral Method for Activity Shaping in Continuous-Time Information Cascades. CoRR abs/1709.05231 (2017) - 2016
- [j14]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): 7-18 (2016) - [j13]Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis:
Suppressing Epidemics in Networks Using Priority Planning. IEEE Trans. Netw. Sci. Eng. 3(4): 271-285 (2016) - [c28]Cédric Malherbe, Emile Contal, Nicolas Vayatis:
A ranking approach to global optimization. ICML 2016: 1539-1547 - [i6]Emile Contal, Nicolas Vayatis:
Stochastic Process Bandits: Upper Confidence Bounds Algorithms via Generic Chaining. CoRR abs/1602.04976 (2016) - 2015
- [c27]Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis:
A Greedy Approach for Dynamic Control of Diffusion Processes in Networks. ICTAI 2015: 652-659 - [c26]Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis:
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks. NIPS 2015: 2026-2034 - 2014
- [j12]Emile Richard, Stéphane Gaïffas, Nicolas Vayatis:
Link prediction in graphs with autoregressive features. J. Mach. Learn. Res. 15(1): 565-593 (2014) - [j11]Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann:
Guest Editors' foreword. Theor. Comput. Sci. 558: 1-4 (2014) - [c25]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: 45-57 - [c24]Emile Contal, Vianney Perchet, Nicolas Vayatis:
Gaussian Process Optimization with Mutual Information. ICML 2014: 253-261 - [c23]Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis:
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. NIPS 2014: 846-854 - [c22]Rémi Lemonnier, Nicolas Vayatis:
Nonparametric Markovian Learning of Triggering Kernels for Mutually Exciting and Mutually Inhibiting Multivariate Hawkes Processes. ECML/PKDD (2) 2014: 161-176 - [i5]Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis:
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. CoRR abs/1407.4744 (2014) - [i4]Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis:
What Makes a Good Plan? An Efficient Planning Approach to Control Diffusion Processes in Networks. CoRR abs/1407.4760 (2014) - 2013
- [j10]Stéphan Clémençon, Marine Depecker, Nicolas Vayatis:
Ranking forests. J. Mach. Learn. Res. 14(1): 39-73 (2013) - [j9]Stéphan Clémençon, Sylvain Robbiano, Nicolas Vayatis:
Ranking data with ordinal labels: optimality and pairwise aggregation. Mach. Learn. 91(1): 67-104 (2013) - [j8]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): 475-496 (2013) - [c21]Emile Contal, David Buffoni, Alexandre Robicquet, Nicolas Vayatis:
Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration. ECML/PKDD (1) 2013: 225-240 - [i3]Emile Contal, David Buffoni, Alexandre Robicquet, Nicolas Vayatis:
Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration. CoRR abs/1304.5350 (2013) - [i2]Emile Contal, Nicolas Vayatis:
Gaussian Process Optimization with Mutual Information. CoRR abs/1311.4825 (2013) - 2012
- [c20]Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann:
Editors' Introduction. ALT 2012: 1-11 - [c19]Pierre-André Savalle, Emile Richard, Nicolas Vayatis:
Estimation of Simultaneously Sparse and Low Rank Matrices. ICML 2012 - [c18]Emile Richard, Stéphane Gaïffas, Nicolas Vayatis:
Link Prediction in Graphs with Autoregressive Features. NIPS 2012: 2843-2851 - [e1]Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann:
Algorithmic Learning Theory - 23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings. Lecture Notes in Computer Science 7568, Springer 2012, ISBN 978-3-642-34105-2 [contents] - [i1]Emile Richard, Andreas Argyriou, Theodoros Evgeniou, Nicolas Vayatis:
A Regularization Approach for Prediction of Edges and Node Features in Dynamic Graphs. CoRR abs/1203.5438 (2012) - 2011
- [j7]Stéphan Clémençon, Marine Depecker, Nicolas Vayatis:
Adaptive partitioning schemes for bipartite ranking - How to grow and prune a ranking tree. Mach. Learn. 83(1): 31-69 (2011) - [j6]Stéphan Clémençon, Marine Depecker, Nicolas Vayatis:
Avancées récentes dans le domaine de l'apprentissage d'ordonnancements. Rev. d'Intelligence Artif. 25(3): 345-368 (2011) - 2010
- [c17]Emile Richard, Nicolas Baskiotis, Theodoros Evgeniou, Nicolas Vayatis:
Link Discovery using Graph Feature Tracking. NIPS 2010: 1966-1974
2000 – 2009
- 2009
- [j5]Stéphan Clémençon, Nicolas Vayatis:
Tree-based ranking methods. IEEE Trans. Inf. Theory 55(9): 4316-4336 (2009) - [c16]Stéphan Clémençon, Nicolas Vayatis:
Adaptive Estimation of the Optimal ROC Curve and a Bipartite Ranking Algorithm. ALT 2009: 216-231 - [c15]Odalric-Ambrym Maillard, Nicolas Vayatis:
Complexity versus Agreement for Many Views. ALT 2009: 232-246 - [c14]Stéphan Clémençon, Nicolas Vayatis:
Nonparametric estimation of the precision-recall curve. ICML 2009: 185-192 - [c13]Stéphan Clémençon, Marine Depecker, Nicolas Vayatis:
Bagging Ranking Trees. ICMLA 2009: 658-663 - [c12]Stéphan Clémençon, Nicolas Vayatis, Marine Depecker:
AUC optimization and the two-sample problem. NIPS 2009: 360-368 - [c11]Stéphan Clémençon, Nicolas Vayatis:
On Partitioning Rules for Bipartite Ranking. AISTATS 2009: 97-104 - 2008
- [c10]Stéphan Clémençon, Nicolas Vayatis:
Approximation of the Optimal ROC Curve and a Tree-Based Ranking Algorithm. ALT 2008: 22-37 - [c9]Patrice Bertail, Stéphan Clémençon, Nicolas Vayatis:
On Bootstrapping the ROC Curve. NIPS 2008: 137-144 - [c8]Stéphan Clémençon, Nicolas Vayatis:
Empirical performance maximization for linear rank statistics. NIPS 2008: 305-312 - [c7]Stéphan Clémençon, Nicolas Vayatis:
Overlaying classifiers: a practical approach for optimal ranking. NIPS 2008: 313-320 - 2007
- [j4]Stéphan Clémençon, Nicolas Vayatis:
Ranking the Best Instances. J. Mach. Learn. Res. 8: 2671-2699 (2007) - 2006
- [j3]Anatoli B. Juditsky, Alexander V. Nazin, Alexandre B. Tsybakov, Nicolas Vayatis:
Remark on "Recursive Aggregation of Estimators by the Mirror Descent Algorithm with Averaging" published in Probl. Peredachi Inf., 2005, no. 4. Probl. Inf. Transm. 42(3): 262 (2006) - 2005
- [j2]Anatoli B. Juditsky, Alexander V. Nazin, Alexandre B. Tsybakov, Nicolas Vayatis:
Recursive Aggregation of Estimators by the Mirror Descent Algorithm with Averaging. Probl. Inf. Transm. 41(4): 368-384 (2005) - [c6]Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis:
Ranking and Scoring Using Empirical Risk Minimization. COLT 2005: 1-15 - [c5]Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis:
From Ranking to Classification: A Statistical View. GfKl 2005: 214-221 - [c4]Anatoli B. Juditsky, Alexander V. Nazin, Alexandre B. Tsybakov, Nicolas Vayatis:
Generalization Error Bounds for Aggregation by Mirror Descent with Averaging. NIPS 2005: 603-610 - 2003
- [j1]Gilles Blanchard, Gábor Lugosi, Nicolas Vayatis:
On the Rate of Convergence of Regularized Boosting Classifiers. J. Mach. Learn. Res. 4: 861-894 (2003) - 2002
- [c3]Gábor Lugosi, Nicolas Vayatis:
A Consistent Strategy for Boosting Algorithms. COLT 2002: 303-318 - 2000
- [c2]Nicolas Vayatis:
The Role of Critical Sets in Vapnik-Chervonenkis Theory. COLT 2000: 75-80
1990 – 1999
- 1999
- [c1]Nicolas Vayatis, Robert Azencott:
Distribution-Dependent Vapnik-Chervonenkis Bounds. EuroCOLT 1999: 230-240
Coauthor Index
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