Distribution-Dependent Vapnik-Chervonenkis Bounds. - Nicolas Vayatis; Robert Azencott (EuroCOLT, 1999)

The Role of Critical Sets in Vapnik-Chervonenkis Theory. - Nicolas Vayatis (COLT, 2000)

A Consistent Strategy for Boosting Algorithms. - Gábor Lugosi; Nicolas Vayatis (COLT, 2002)

On the Rate of Convergence of Regularized Boosting Classifiers. - Gilles Blanchard; Gábor Lugosi; Nicolas Vayatis (J. Mach. Learn. Res., 2003)

Ranking and Scoring Using Empirical Risk Minimization. - Stéphan Clémençon; Gábor Lugosi; Nicolas Vayatis (COLT, 2005)

From Ranking to Classification: A Statistical View. - Stéphan Clémençon; Gábor Lugosi; Nicolas Vayatis (GfKl, 2005)

Generalization Error Bounds for Aggregation by Mirror Descent with Averaging. - Anatoli B. Juditsky; Alexander V. Nazin; Alexandre B. Tsybakov; Nicolas Vayatis (NIPS, 2005)

Recursive Aggregation of Estimators by the Mirror Descent Algorithm with Averaging. - Anatoli B. Juditsky; Alexander V. Nazin; Alexandre B. Tsybakov; Nicolas Vayatis (Probl. Inf. Transm., 2005)

Remark on "Recursive Aggregation of Estimators by the Mirror Descent Algorithm with Averaging" published in Probl. Peredachi Inf., 2005, no. 4. - Anatoli B. Juditsky; Alexander V. Nazin; Alexandre B. Tsybakov; Nicolas Vayatis (Probl. Inf. Transm., 2006)

Ranking the Best Instances. - Stéphan Clémençon; Nicolas Vayatis (J. Mach. Learn. Res., 2007)

Approximation of the Optimal ROC Curve and a Tree-Based Ranking Algorithm. - Stéphan Clémençon; Nicolas Vayatis (ALT, 2008)

On Bootstrapping the ROC Curve. - Patrice Bertail; Stéphan Clémençon; Nicolas Vayatis (NIPS, 2008)

Empirical performance maximization for linear rank statistics. - Stéphan Clémençon; Nicolas Vayatis (NIPS, 2008)

Overlaying classifiers: a practical approach for optimal ranking. - Stéphan Clémençon; Nicolas Vayatis (NIPS, 2008)

Adaptive Estimation of the Optimal ROC Curve and a Bipartite Ranking Algorithm. - Stéphan Clémençon; Nicolas Vayatis (ALT, 2009)

Complexity versus Agreement for Many Views. - Odalric-Ambrym Maillard; Nicolas Vayatis (ALT, 2009)

Nonparametric estimation of the precision-recall curve. - Stéphan Clémençon; Nicolas Vayatis (ICML, 2009)

Bagging Ranking Trees. - Stéphan Clémençon; Marine Depecker; Nicolas Vayatis (ICMLA, 2009)

AUC optimization and the two-sample problem. - Stéphan Clémençon; Nicolas Vayatis; Marine Depecker (NIPS, 2009)

On Partitioning Rules for Bipartite Ranking. - Stéphan Clémençon; Nicolas Vayatis (AISTATS, 2009)

Tree-based ranking methods. - Stéphan Clémençon; Nicolas Vayatis (IEEE Trans. Inf. Theory, 2009)

Link Discovery using Graph Feature Tracking. - Emile Richard; Nicolas Baskiotis; Theodoros Evgeniou; Nicolas Vayatis (NIPS, 2010)

Adaptive partitioning schemes for bipartite ranking - How to grow and prune a ranking tree. - Stéphan Clémençon; Marine Depecker; Nicolas Vayatis (Mach. Learn., 2011)

Avancées récentes dans le domaine de l'apprentissage d'ordonnancements. - Stéphan Clémençon; Marine Depecker; Nicolas Vayatis (Rev. d'Intelligence Artif., 2011)

Editors' Introduction. - Nader H. Bshouty; Gilles Stoltz; Nicolas Vayatis; Thomas Zeugmann (ALT, 2012)

Estimation of Simultaneously Sparse and Low Rank Matrices. - Pierre-André Savalle; Emile Richard; Nicolas Vayatis (ICML, 2012)

A Regularization Approach for Prediction of Edges and Node Features in Dynamic Graphs - Emile Richard; Andreas Argyriou; Theodoros Evgeniou; Nicolas Vayatis (CoRR, 2012)

Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration. - Emile Contal; David Buffoni; Alexandre Robicquet; Nicolas Vayatis (ECML/PKDD (1), 2013)

Gaussian Process Optimization with Mutual Information. - Emile Contal; Nicolas Vayatis (CoRR, 2013)

Ranking forests. - Stéphan Clémençon; Marine Depecker; Nicolas Vayatis (J. Mach. Learn. Res., 2013)

Ranking data with ordinal labels: optimality and pairwise aggregation. - Stéphan Clémençon; Sylvain Robbiano; Nicolas Vayatis (Mach. Learn., 2013)

An empirical comparison of learning algorithms for nonparametric scoring: the TreeRank algorithm and other methods. - Stéphan Clémençon; Marine Depecker; Nicolas Vayatis (Pattern Anal. Appl., 2013)

A New Framework for the Simulation of Offshore Oil Facilities at the System Level. - Marc Bonnissel; Joris Costes; Jean-Michel Ghidaglia; Philippe Muguerra; Keld Lund Nielsen; Benjamin Poirson; Xavier Riou; Jean-Philippe Saut; Nicolas Vayatis (CSDM, 2014)

Gaussian Process Optimization with Mutual Information. - Emile Contal; Vianney Perchet; Nicolas Vayatis (ICML, 2014)

Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. - Rémi Lemonnier; Kevin Scaman; Nicolas Vayatis (NIPS, 2014)

Nonparametric Markovian Learning of Triggering Kernels for Mutually Exciting and Mutually Inhibiting Multivariate Hawkes Processes. - Rémi Lemonnier; Nicolas Vayatis (ECML/PKDD (2), 2014)

What Makes a Good Plan? An Efficient Planning Approach to Control Diffusion Processes in Networks. - Kevin Scaman; Argyris Kalogeratos; Nicolas Vayatis (CoRR, 2014)

Link prediction in graphs with autoregressive features. - Emile Richard; Stéphane Gaïffas; Nicolas Vayatis (J. Mach. Learn. Res., 2014)

Guest Editors' foreword. - Nader H. Bshouty; Gilles Stoltz; Nicolas Vayatis; Thomas Zeugmann (Theor. Comput. Sci., 2014)

A Greedy Approach for Dynamic Control of Diffusion Processes in Networks. - Kevin Scaman; Argyris Kalogeratos; Nicolas Vayatis (ICTAI, 2015)

Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks. - Kevin Scaman; Rémi Lemonnier; Nicolas Vayatis (NIPS, 2015)

A ranking approach to global optimization. - Cédric Malherbe; Emile Contal; Nicolas Vayatis (ICML, 2016)

Stochastic Process Bandits: Upper Confidence Bounds Algorithms via Generic Chaining. - Emile Contal; Nicolas Vayatis (CoRR, 2016)

Quantitative Analysis of Dynamic Fault Trees Based on the Coupling of Structure Functions and Monte Carlo Simulation. - Guillaume Merle; Jean-Marc Roussel; Jean-Jacques Lesage; Vianney Perchet; Nicolas Vayatis (Qual. Reliab. Eng. Int., 2016)

Suppressing Epidemics in Networks Using Priority Planning. - Kevin Scaman; Argyris Kalogeratos; Nicolas Vayatis (IEEE Trans. Netw. Sci. Eng., 2016)

Fall detection using smart floor sensor and supervised learning. - Ludovic Minvielle; Mounir Atiq; Renan Serra; Mathilde Mougeot; Nicolas Vayatis (EMBC, 2017)

Penalty learning for changepoint detection. - Charles Truong; Laurent Oudre; Nicolas Vayatis (EUSIPCO, 2017)

Global optimization of Lipschitz functions. - Cédric Malherbe; Nicolas Vayatis (ICML, 2017)

Distributed Convolutional Sparse Coding. - Thomas Moreau 001; Laurent Oudre; Nicolas Vayatis (CoRR, 2017)

A Spectral Method for Activity Shaping in Continuous-Time Information Cascades. - Kevin Scaman; Argyris Kalogeratos; Luca Corinzia; Nicolas Vayatis (CoRR, 2017)

The Complementary Role of Activity Context in the Mental Workload Evaluation of Helicopter Pilots: A Multi-tasking Learning Approach. - Ioannis Bargiotas; Alice Nicolaï; Pierre-Paul Vidal; Christophe Labourdette; Nicolas Vayatis; Stéphane Buffat (H-WORKLOAD, 2018)

DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding. - Thomas Moreau 001; Laurent Oudre; Nicolas Vayatis (ICML, 2018)

A review of change point detection methods. - Charles Truong; Laurent Oudre; Nicolas Vayatis (CoRR, 2018)

ruptures: change point detection in Python. - Charles Truong; Laurent Oudre; Nicolas Vayatis (CoRR, 2018)

The Multi-Round Sequential Selection Problem. - Mathilde Fekom; Argyris Kalogeratos; Nicolas Vayatis (CoRR, 2018)

Template-Based Step Detection with Inertial Measurement Units. - Laurent Oudre; Rémi Barrois; Thomas Moreau 001; Charles Truong; Aliénor Vienne-Jumeau; Damien Ricard; Nicolas Vayatis; Pierre-Paul Vidal (Sensors, 2018)

Sequential Dynamic Resource Allocation for Epidemic Control. - Mathilde Fekom; Nicolas Vayatis; Argyris Kalogeratos (CDC, 2019)

Subsampling of Multivariate Time-Vertex Graph Signals. - Pierre Humbert; Laurent Oudre; Nicolas Vayatis (EUSIPCO, 2019)

Supervised Kernel Change Point Detection with Partial Annotations. - Charles Truong; Laurent Oudre; Nicolas Vayatis (ICASSP, 2019)

Optimal Multiple Stopping Rule for Warm-Starting Sequential Selection. - Mathilde Fekom; Nicolas Vayatis; Argyris Kalogeratos (ICTAI, 2019)

Revealing posturographic features associated with the risk of falling in patients with Parkinsonian syndromes via machine learning. - Ioannis Bargiotas; Argyris Kalogeratos; Myrto Limnios; Pierre-Paul Vidal; Damien Ricard; Nicolas Vayatis (CoRR, 2019)

Multivariate Convolutional Sparse Coding with Low Rank Tensor. - Pierre Humbert; Julien Audiffren; Laurent Oudre; Nicolas Vayatis (CoRR, 2019)

Detecting multiple change-points in the time-varying Ising model. - Batiste Le Bars; Pierre Humbert; Argyris Kalogeratos; Nicolas Vayatis (CoRR, 2019)

Selection of the Best Electroencephalogram Channel to Predict the Depth of Anesthesia. - Clément Dubost; Pierre Humbert; Arno Benizri; Jean-Pierre Tourtier; Nicolas Vayatis; Pierre-Paul Vidal (Frontiers Comput. Neurosci., 2019)

Local Assessment of Statokinesigram Dynamics in Time: An in-Depth Look at the Scoring Algorithm. - Ioannis Bargiotas; Julien Audiffren; Nicolas Vayatis; Pierre-Paul Vidal; Alain P. Yelnik; Damien Ricard (Image Process. Line, 2019)

A Data Set for the Study of Human Locomotion with Inertial Measurements Units. - Charles Truong; Rémi Barrois-Müller; Thomas Moreau 001; Clément Provost; Aliénor Vienne-Jumeau; Albane Moreau; Pierre-Paul Vidal; Nicolas Vayatis; Stéphane Buffat; Alain P. Yelnik; Damien Ricard; Laurent Oudre (Image Process. Line, 2019)

Greedy Kernel Change-Point Detection. - Charles Truong; Laurent Oudre; Nicolas Vayatis (IEEE Trans. Signal Process., 2019)

Localized Interpolation for Graph Signals. - Antoine Mazarguil; Laurent Oudre; Nicolas Vayatis (EUSIPCO, 2020)

Low Rank Activations for Tensor-Based Convolutional Sparse Coding. - Pierre Humbert; Julien Audiffren; Laurent Oudre; Nicolas Vayatis (ICASSP, 2020)

Learning the piece-wise constant graph structure of a varying Ising model. - Batiste Le Bars; Pierre Humbert; Argyris Kalogeratos; Nicolas Vayatis (ICML, 2020)

Unsupervised Multi-source Domain Adaptation for Regression. - Guillaume Richard; Antoine de Mathelin; Georges Hébrail; Mathilde Mougeot; Nicolas Vayatis (ECML/PKDD (1), 2020)

Multivariate two-sample hypothesis testing through AUC maximization for biomedical applications. - Ioannis Bargiotas; Argyris Kalogeratos; Myrto Limnios; Pierre-Paul Vidal; Damien Ricard; Nicolas Vayatis (SETN, 2020)

Dynamic Epidemic Control via Sequential Resource Allocation. - Mathilde Fekom; Nicolas Vayatis; Argyris Kalogeratos (CoRR, 2020)

Adversarial Weighting for Domain Adaptation in Regression. - Antoine de Mathelin; Guillaume Richard; Mathilde Mougeot; Nicolas Vayatis (CoRR, 2020)

Tensor Convolutional Sparse Coding with Low-Rank activations, an application to EEG analysis. - Pierre Humbert; Laurent Oudre; Nicolas Vayatis; Julien Audiffren (CoRR, 2020)

SEAIR Framework Accounting for a Personalized Risk Prediction Score: Application to the Covid-19 Epidemic. - Olivier Boulant; Mathilde Fekom; Camille Pouchol; Theodoros Evgeniou; Anton Ovchinnikov; Raphaël Porcher; Nicolas Vayatis (Image Process. Line, 2020)

The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review. - Sylvain Jung; Mona Michaud; Laurent Oudre; Eric Dorveaux; Louis Gorintin; Nicolas Vayatis; Damien Ricard (Sensors, 2020)

Selective review of offline change point detection methods. - Charles Truong; Laurent Oudre; Nicolas Vayatis (Signal Process., 2020)

Offline detection of change-points in the mean for stationary graph signals. - Alejandro de la Concha; Nicolas Vayatis; Argyris Kalogeratos (AISTATS, 2021)

Adaptive Change-Point Detection for Studying Human Locomotion. - Sylvain Jung; Laurent Oudre; Charles Truong; Eric Dorveaux; Louis Gorintin; Nicolas Vayatis; Damien Ricard (EMBC, 2021)

Bayesian Feature Discovery for Predictive Maintenance. - Amir Dib; Charles Truong; Laurent Oudre; Mathilde Mougeot; Nicolas Vayatis; Heloïse Nonne (EUSIPCO, 2021)

Adversarial Weighting for Domain Adaptation in Regression. - Antoine de Mathelin; Guillaume Richard; François Deheeger; Mathilde Mougeot; Nicolas Vayatis (ICTAI, 2021)

Discrepancy-Based Active Learning for Domain Adaptation. - Antoine de Mathelin; Mathilde Mougeot; Nicolas Vayatis (CoRR, 2021)

ADAPT : Awesome Domain Adaptation Python Toolbox. - Antoine de Mathelin; François Deheeger; Guillaume Richard; Mathilde Mougeot; Nicolas Vayatis (CoRR, 2021)

Online non-parametric change-point detection for heterogeneous data streams observed over graph nodes. - Alejandro de la Concha; Argyris Kalogeratos; Nicolas Vayatis (CoRR, 2021)

A Compartmental Epidemiological Model Applied to the Covid-19 Epidemic. - Anne-Flore Baron; Olivier Boulant; Ivan Panico; Nicolas Vayatis (Image Process. Line, 2021)

Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation. - Pierre Humbert; Batiste Le Bars; Laurent Oudre; Argyris Kalogeratos; Nicolas Vayatis (J. Mach. Learn. Res., 2021)

Discrepancy-Based Active Learning for Domain Adaptation. - Antoine de Mathelin; François Deheeger; Mathilde Mougeot; Nicolas Vayatis (ICLR, 2022)

Fast and Accurate Importance Weighting for Correcting Sample Bias. - Antoine de Mathelin; François Deheeger; Mathilde Mougeot; Nicolas Vayatis (ECML/PKDD (1), 2022)

Collaborative likelihood-ratio estimation over graphs. - Alejandro de la Concha; Argyris Kalogeratos; Nicolas Vayatis (CoRR, 2022)

Non-smooth interpolation of graph signals. - Antoine Mazarguil; Laurent Oudre; Nicolas Vayatis (Signal Process., 2022)

An Uncertainty Principle for Lowband Graph Signals. - Antoine Mazarguil; Laurent Oudre; Nicolas Vayatis (IEEE Signal Process. Lett., 2022)

Tensor Convolutional Dictionary Learning With CP Low-Rank Activations. - Pierre Humbert; Laurent Oudre; Nicolas Vayatis; Julien Audiffren (IEEE Trans. Signal Process., 2022)

A Framework for Paired-Sample Hypothesis Testing for High-Dimensional Data. - Ioannis Bargiotas; Argyris Kalogeratos; Nicolas Vayatis (ICTAI, 2023)

Personalized One-Shot Collaborative Learning. - Marie Garin; Antoine de Mathelin; Mathilde Mougeot; Nicolas Vayatis (ICTAI, 2023)

Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation. - Alejandro de la Concha; Argyris Kalogeratos; Nicolas Vayatis (CoRR, 2023)

Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification. - Antoine de Mathelin; François Deheeger; Mathilde Mougeot; Nicolas Vayatis (CoRR, 2023)

Maximum Weight Entropy. - Antoine de Mathelin; François Deheeger; Mathilde Mougeot; Nicolas Vayatis (CoRR, 2023)

A Data Set for Fall Detection with Smart Floor Sensors. - Charles Truong; Mounir Atiq; Ludovic Minvielle; Renan Serra; Mathilde Mougeot; Nicolas Vayatis (Image Process. Line, 2023)

Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization. - Alejandro D. de la Concha Duarte; Nicolas Vayatis; Argyris Kalogeratos (AISTATS, 2024)

LIPO+: Frugal Global Optimization for Lipschitz Functions. - Gaëtan Serré; Perceval Beja-Battais; Sophia Chirrane; Argyris Kalogeratos; Nicolas Vayatis (SETN, 2024)

A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents. - Jean Vassoyan; Anan Schütt; Jill-Jênn Vie; Arun-Balajiee Lekshmi-Narayanan; Elisabeth André; Nicolas Vayatis (CoRR, 2024)

Arm-CODA: A Data Set of Upper-limb Human Movement During Routine Examination. - 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 (Image Process. Line, 2024)

OneBatchPAM: A Fast and Frugal K-Medoids Algorithm. - Antoine de Mathelin; Nicolas Enrique Cecchi; François Deheeger; Mathilde Mougeot; Nicolas Vayatis (AAAI, 2025)

Collaborative non-parametric two-sample testing. - Alejandro D. de la Concha Duarte; Nicolas Vayatis; Argyris Kalogeratos (AISTATS, 2025)

Stein Boltzmann Sampling: A Variational Approach for Global Optimization. - Gaëtan Serré; Argyris Kalogeratos; Nicolas Vayatis (AISTATS, 2025)

Assessing virtual reality presence through physiological measures: a comprehensive review. - Danping Wang; Yunchao Peng; Lise Haddouk; Nicolas Vayatis; Pierre-Paul Vidal (Frontiers Virtual Real., 2025)