Nicolas Vayatis

124 papers A* 12A 5B 10C 1Misc 2Journal 67Unranked 27

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Optimal Fair Aggregation of Crowdsourced Noisy Labels using Demographic Parity Constraints. CoRR Gabriel Singer, Samuel Gruffaz, Olivier Vo Van, Nicolas Vayatis, Argyris Kalogeratos
2025	J	`jnl`	Assessing virtual reality presence through physiological measures: a comprehensive review. Frontiers Virtual Real. Danping Wang, Yunchao Peng, Lise Haddouk, Nicolas Vayatis, Pierre-Paul Vidal
2025	J	`jnl`	Collaborative likelihood-ratio estimation over graphs. J. Mach. Learn. Res. Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
2025	A	`conf`	Collaborative non-parametric two-sample testing. AISTATS Alejandro D. de la Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos
2025	J	`jnl`	Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization. J. Mach. Learn. Res. Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
2025	J	`jnl`	Enhancing Nuclear Reactor Core Simulation through Data-Based Surrogate Models. CoRR Perceval Beja-Battais, Alain Grossetête, Nicolas Vayatis
2025	A*	`conf`	OneBatchPAM: A Fast and Frugal K-Medoids Algorithm. AAAI Antoine de Mathelin, Nicolas Enrique Cecchi, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
2025	J	`jnl`	OneBatchPAM: A Fast and Frugal K-Medoids Algorithm. CoRR Antoine de Mathelin, Nicolas Enrique Cecchi, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
2025	—	`conf`	Online Non-parametric Change-point Detection via Graph-based Likelihood-Ratio Estimation. EUSIPCO Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
2025	A	`conf`	Stein Boltzmann Sampling: A Variational Approach for Global Optimization. AISTATS Gaëtan Serré, Argyris Kalogeratos, Nicolas Vayatis
2024	J	`jnl`	A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents. CoRR Jean Vassoyan, Anan Schütt, Jill-Jênn Vie, Arun-Balajiee Lekshmi-Narayanan, Elisabeth André, Nicolas Vayatis
2024	J	`jnl`	Arm-CODA: A Data Set of Upper-limb Human Movement During Routine Examination. Image Process. Line 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
2024	J	`jnl`	Collaborative non-parametric two-sample testing. CoRR Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
2024	—	`conf`	LIPO+: Frugal Global Optimization for Lipschitz Functions. SETN Gaëtan Serré, Perceval Beja-Battais, Sophia Chirrane, Argyris Kalogeratos, Nicolas Vayatis
2024	A	`conf`	Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization. AISTATS Alejandro D. de la Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos
2023	J	`jnl`	A Data Set for Fall Detection with Smart Floor Sensors. Image Process. Line Charles Truong, Mounir Atiq, Ludovic Minvielle, Renan Serra, Mathilde Mougeot, Nicolas Vayatis
2023	B	`conf`	A Framework for Paired-Sample Hypothesis Testing for High-Dimensional Data. ICTAI Ioannis Bargiotas, Argyris Kalogeratos, Nicolas Vayatis
2023	J	`jnl`	A framework for paired-sample hypothesis testing for high-dimensional data. CoRR Ioannis Bargiotas, Argyris Kalogeratos, Nicolas Vayatis
2023	J	`jnl`	Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification. CoRR Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
2023	J	`jnl`	Maximum Weight Entropy. CoRR Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
2023	J	`jnl`	Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation. CoRR Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis
2023	J	`jnl`	Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization. CoRR Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
2023	B	`conf`	Personalized One-Shot Collaborative Learning. ICTAI Marie Garin, Antoine de Mathelin, Mathilde Mougeot, Nicolas Vayatis
2022	J	`jnl`	An Uncertainty Principle for Lowband Graph Signals. IEEE Signal Process. Lett. Antoine Mazarguil, Laurent Oudre, Nicolas Vayatis
2022	J	`jnl`	Collaborative likelihood-ratio estimation over graphs. CoRR Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis
2022	A*	`conf`	Discrepancy-Based Active Learning for Domain Adaptation. ICLR Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
2022	—	`conf`	Fast and Accurate Importance Weighting for Correcting Sample Bias. ECML/PKDD (1) Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
2022	J	`jnl`	Fast and Accurate Importance Weighting for Correcting Sample Bias. CoRR Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
2022	J	`jnl`	Non-smooth interpolation of graph signals. Signal Process. Antoine Mazarguil, Laurent Oudre, Nicolas Vayatis
2022	J	`jnl`	Tensor Convolutional Dictionary Learning With CP Low-Rank Activations. IEEE Trans. Signal Process. Pierre Humbert, Laurent Oudre, Nicolas Vayatis, Julien Audiffren
2021	J	`jnl`	A Compartmental Epidemiological Model Applied to the Covid-19 Epidemic. Image Process. Line Anne-Flore Baron, Olivier Boulant, Ivan Panico, Nicolas Vayatis
2021	J	`jnl`	ADAPT : Awesome Domain Adaptation Python Toolbox. CoRR Antoine de Mathelin, François Deheeger, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis
2021	—	`conf`	Adaptive Change-Point Detection for Studying Human Locomotion. EMBC Sylvain Jung, Laurent Oudre, Charles Truong, Eric Dorveaux, Louis Gorintin, Nicolas Vayatis, Damien Ricard
2021	B	`conf`	Adversarial Weighting for Domain Adaptation in Regression. ICTAI Antoine de Mathelin, Guillaume Richard, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
2021	—	`conf`	Bayesian Feature Discovery for Predictive Maintenance. EUSIPCO Amir Dib, Charles Truong, Laurent Oudre, Mathilde Mougeot, Nicolas Vayatis, Heloïse Nonne
2021	J	`jnl`	Discrepancy-Based Active Learning for Domain Adaptation. CoRR Antoine de Mathelin, Mathilde Mougeot, Nicolas Vayatis
2021	J	`jnl`	Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation. J. Mach. Learn. Res. Pierre Humbert, Batiste Le Bars, Laurent Oudre, Argyris Kalogeratos, Nicolas Vayatis
2021	A	`conf`	Offline detection of change-points in the mean for stationary graph signals. AISTATS Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
2021	J	`jnl`	Online non-parametric change-point detection for heterogeneous data streams observed over graph nodes. CoRR Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis
2020	J	`jnl`	Adversarial Weighting for Domain Adaptation in Regression. CoRR Antoine de Mathelin, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis
2020	J	`jnl`	Dynamic Epidemic Control via Sequential Resource Allocation. CoRR Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos
2020	A*	`conf`	Learning the piece-wise constant graph structure of a varying Ising model. ICML Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis
2020	—	`conf`	Localized Interpolation for Graph Signals. EUSIPCO Antoine Mazarguil, Laurent Oudre, Nicolas Vayatis
2020	Misc	`conf`	Low Rank Activations for Tensor-Based Convolutional Sparse Coding. ICASSP Pierre Humbert, Julien Audiffren, Laurent Oudre, Nicolas Vayatis
2020	—	`conf`	Multivariate two-sample hypothesis testing through AUC maximization for biomedical applications. SETN Ioannis Bargiotas, Argyris Kalogeratos, Myrto Limnios, Pierre-Paul Vidal, Damien Ricard, Nicolas Vayatis
2020	J	`jnl`	Offline detection of change-points in the mean for stationary graph signals. CoRR Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
2020	J	`jnl`	Optimal Multiple Stopping Rule for Warm-Starting Sequential Selection. CoRR Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos
2020	J	`jnl`	SEAIR Framework Accounting for a Personalized Risk Prediction Score: Application to the Covid-19 Epidemic. Image Process. Line Olivier Boulant, Mathilde Fekom, Camille Pouchol, Theodoros Evgeniou, Anton Ovchinnikov, Raphaël Porcher, Nicolas Vayatis
2020	J	`jnl`	Selective review of offline change point detection methods. Signal Process. Charles Truong, Laurent Oudre, Nicolas Vayatis
2020	J	`jnl`	Tensor Convolutional Sparse Coding with Low-Rank activations, an application to EEG analysis. CoRR Pierre Humbert, Laurent Oudre, Nicolas Vayatis, Julien Audiffren
2020	J	`jnl`	The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review. Sensors Sylvain Jung, Mona Michaud, Laurent Oudre, Eric Dorveaux, Louis Gorintin, Nicolas Vayatis, Damien Ricard
2020	—	`conf`	Unsupervised Multi-source Domain Adaptation for Regression. ECML/PKDD (1) Guillaume Richard, Antoine de Mathelin, Georges Hébrail, Mathilde Mougeot, Nicolas Vayatis
2019	J	`jnl`	A Data Set for the Study of Human Locomotion with Inertial Measurements Units. Image Process. Line 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
2019	J	`jnl`	Detecting multiple change-points in the time-varying Ising model. CoRR Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis
2019	J	`jnl`	Greedy Kernel Change-Point Detection. IEEE Trans. Signal Process. Charles Truong, Laurent Oudre, Nicolas Vayatis
2019	J	`jnl`	Local Assessment of Statokinesigram Dynamics in Time: An in-Depth Look at the Scoring Algorithm. Image Process. Line Ioannis Bargiotas, Julien Audiffren, Nicolas Vayatis, Pierre-Paul Vidal, Alain P. Yelnik, Damien Ricard
2019	J	`jnl`	Multivariate Convolutional Sparse Coding with Low Rank Tensor. CoRR Pierre Humbert, Julien Audiffren, Laurent Oudre, Nicolas Vayatis
2019	B	`conf`	Optimal Multiple Stopping Rule for Warm-Starting Sequential Selection. ICTAI Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos
2019	J	`jnl`	Revealing posturographic features associated with the risk of falling in patients with Parkinsonian syndromes via machine learning. CoRR Ioannis Bargiotas, Argyris Kalogeratos, Myrto Limnios, Pierre-Paul Vidal, Damien Ricard, Nicolas Vayatis
2019	J	`jnl`	Selection of the Best Electroencephalogram Channel to Predict the Depth of Anesthesia. Frontiers Comput. Neurosci. Clément Dubost, Pierre Humbert, Arno Benizri, Jean-Pierre Tourtier, Nicolas Vayatis, Pierre-Paul Vidal
2019	—	`conf`	Sequential Dynamic Resource Allocation for Epidemic Control. CDC Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos
2019	J	`jnl`	Sequential Dynamic Resource Allocation for Epidemic Control. CoRR Mathilde Fekom, Nicolas Vayatis, Argyris Kalogeratos
2019	—	`conf`	Subsampling of Multivariate Time-Vertex Graph Signals. EUSIPCO Pierre Humbert, Laurent Oudre, Nicolas Vayatis
2019	Misc	`conf`	Supervised Kernel Change Point Detection with Partial Annotations. ICASSP Charles Truong, Laurent Oudre, Nicolas Vayatis
2018	J	`jnl`	A review of change point detection methods. CoRR Charles Truong, Laurent Oudre, Nicolas Vayatis
2018	A*	`conf`	DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding. ICML Thomas Moreau, Laurent Oudre, Nicolas Vayatis
2018	J	`jnl`	Template-Based Step Detection with Inertial Measurement Units. Sensors Laurent Oudre, Rémi Barrois, Thomas Moreau, Charles Truong, Aliénor Vienne-Jumeau, Damien Ricard, Nicolas Vayatis, Pierre-Paul Vidal
2018	—	`conf`	The Complementary Role of Activity Context in the Mental Workload Evaluation of Helicopter Pilots: A Multi-tasking Learning Approach. H-WORKLOAD Ioannis Bargiotas, Alice Nicolaï, Pierre-Paul Vidal, Christophe Labourdette, Nicolas Vayatis, Stéphane Buffat
2018	J	`jnl`	The Multi-Round Sequential Selection Problem. CoRR Mathilde Fekom, Argyris Kalogeratos, Nicolas Vayatis
2018	J	`jnl`	ruptures: change point detection in Python. CoRR Charles Truong, Laurent Oudre, Nicolas Vayatis
2017	J	`jnl`	A Spectral Method for Activity Shaping in Continuous-Time Information Cascades. CoRR Kevin Scaman, Argyris Kalogeratos, Luca Corinzia, Nicolas Vayatis
2017	J	`jnl`	Distributed Convolutional Sparse Coding. CoRR Thomas Moreau, Laurent Oudre, Nicolas Vayatis
2017	—	`conf`	Fall detection using smart floor sensor and supervised learning. EMBC Ludovic Minvielle, Mounir Atiq, Renan Serra, Mathilde Mougeot, Nicolas Vayatis
2017	A*	`conf`	Global optimization of Lipschitz functions. ICML Cédric Malherbe, Nicolas Vayatis
2017	—	`conf`	Penalty learning for changepoint detection. EUSIPCO Charles Truong, Laurent Oudre, Nicolas Vayatis
2016	A*	`conf`	A ranking approach to global optimization. ICML Cédric Malherbe, Emile Contal, Nicolas Vayatis
2016	J	`jnl`	Quantitative Analysis of Dynamic Fault Trees Based on the Coupling of Structure Functions and Monte Carlo Simulation. Qual. Reliab. Eng. Int. Guillaume Merle, Jean-Marc Roussel, Jean-Jacques Lesage, Vianney Perchet, Nicolas Vayatis
2016	J	`jnl`	Stochastic Process Bandits: Upper Confidence Bounds Algorithms via Generic Chaining. CoRR Emile Contal, Nicolas Vayatis
2016	J	`jnl`	Suppressing Epidemics in Networks Using Priority Planning. IEEE Trans. Netw. Sci. Eng. Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis
2015	B	`conf`	A Greedy Approach for Dynamic Control of Diffusion Processes in Networks. ICTAI Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis
2015	—	`conf`	Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks. NIPS Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis
2014	—	`conf`	A New Framework for the Simulation of Offshore Oil Facilities at the System Level. CSDM Marc Bonnissel, Joris Costes, Jean-Michel Ghidaglia, Philippe Muguerra, Keld Lund Nielsen, Benjamin Poirson, Xavier Riou, Jean-Philippe Saut, Nicolas Vayatis
2014	A*	`conf`	Gaussian Process Optimization with Mutual Information. ICML Emile Contal, Vianney Perchet, Nicolas Vayatis
2014	J	`jnl`	Guest Editors' foreword. Theor. Comput. Sci. Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann
2014	J	`jnl`	Link prediction in graphs with autoregressive features. J. Mach. Learn. Res. Emile Richard, Stéphane Gaïffas, Nicolas Vayatis
2014	—	`conf`	Nonparametric Markovian Learning of Triggering Kernels for Mutually Exciting and Mutually Inhibiting Multivariate Hawkes Processes. ECML/PKDD (2) Rémi Lemonnier, Nicolas Vayatis
2014	—	`conf`	Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. NIPS Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis
2014	J	`jnl`	Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology. CoRR Rémi Lemonnier, Kevin Scaman, Nicolas Vayatis
2014	J	`jnl`	What Makes a Good Plan? An Efficient Planning Approach to Control Diffusion Processes in Networks. CoRR Kevin Scaman, Argyris Kalogeratos, Nicolas Vayatis
2013	J	`jnl`	An empirical comparison of learning algorithms for nonparametric scoring: the TreeRank algorithm and other methods. Pattern Anal. Appl. Stéphan Clémençon, Marine Depecker, Nicolas Vayatis
2013	J	`jnl`	Gaussian Process Optimization with Mutual Information. CoRR Emile Contal, Nicolas Vayatis
2013	J	`jnl`	Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration CoRR Emile Contal, David Buffoni, Alexandre Robicquet, Nicolas Vayatis
2013	—	`conf`	Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration. ECML/PKDD (1) Emile Contal, David Buffoni, Alexandre Robicquet, Nicolas Vayatis
2013	J	`jnl`	Ranking data with ordinal labels: optimality and pairwise aggregation. Mach. Learn. Stéphan Clémençon, Sylvain Robbiano, Nicolas Vayatis
2013	J	`jnl`	Ranking forests. J. Mach. Learn. Res. Stéphan Clémençon, Marine Depecker, Nicolas Vayatis
2012	J	`jnl`	A Regularization Approach for Prediction of Edges and Node Features in Dynamic Graphs CoRR Emile Richard, Andreas Argyriou, Theodoros Evgeniou, Nicolas Vayatis
2012	B	`ed.`	Algorithmic Learning Theory - 23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings ALT Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann
2012	B	`conf`	Editors' Introduction. ALT Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann
2012	A*	`conf`	Estimation of Simultaneously Sparse and Low Rank Matrices. ICML Pierre-André Savalle, Emile Richard, Nicolas Vayatis
2012	—	`conf`	Link Prediction in Graphs with Autoregressive Features. NIPS Emile Richard, Stéphane Gaïffas, Nicolas Vayatis
2011	J	`jnl`	Adaptive partitioning schemes for bipartite ranking - How to grow and prune a ranking tree. Mach. Learn. Stéphan Clémençon, Marine Depecker, Nicolas Vayatis
2011	J	`jnl`	Avancées récentes dans le domaine de l'apprentissage d'ordonnancements. Rev. d'Intelligence Artif. Stéphan Clémençon, Marine Depecker, Nicolas Vayatis
2010	—	`conf`	Link Discovery using Graph Feature Tracking. NIPS Emile Richard, Nicolas Baskiotis, Theodoros Evgeniou, Nicolas Vayatis
2009	—	`conf`	AUC optimization and the two-sample problem. NIPS Stéphan Clémençon, Nicolas Vayatis, Marine Depecker
2009	B	`conf`	Adaptive Estimation of the Optimal ROC Curve and a Bipartite Ranking Algorithm. ALT Stéphan Clémençon, Nicolas Vayatis
2009	C	`conf`	Bagging Ranking Trees. ICMLA Stéphan Clémençon, Marine Depecker, Nicolas Vayatis
2009	B	`conf`	Complexity versus Agreement for Many Views. ALT Odalric-Ambrym Maillard, Nicolas Vayatis
2009	A*	`conf`	Nonparametric estimation of the precision-recall curve. ICML Stéphan Clémençon, Nicolas Vayatis
2009	A	`conf`	On Partitioning Rules for Bipartite Ranking. AISTATS Stéphan Clémençon, Nicolas Vayatis
2009	J	`jnl`	Tree-based ranking methods. IEEE Trans. Inf. Theory Stéphan Clémençon, Nicolas Vayatis
2008	B	`conf`	Approximation of the Optimal ROC Curve and a Tree-Based Ranking Algorithm. ALT Stéphan Clémençon, Nicolas Vayatis
2008	—	`conf`	Empirical performance maximization for linear rank statistics. NIPS Stéphan Clémençon, Nicolas Vayatis
2008	—	`conf`	On Bootstrapping the ROC Curve. NIPS Patrice Bertail, Stéphan Clémençon, Nicolas Vayatis
2008	—	`conf`	Overlaying classifiers: a practical approach for optimal ranking. NIPS Stéphan Clémençon, Nicolas Vayatis
2007	J	`jnl`	Ranking the Best Instances. J. Mach. Learn. Res. Stéphan Clémençon, Nicolas Vayatis
2006	J	`jnl`	Remark on "Recursive Aggregation of Estimators by the Mirror Descent Algorithm with Averaging" published in Probl. Inf. Transm. Anatoli B. Juditsky, Alexander V. Nazin, Alexandre B. Tsybakov, Nicolas Vayatis
2005	—	`conf`	From Ranking to Classification: A Statistical View. GfKl Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis
2005	—	`conf`	Generalization Error Bounds for Aggregation by Mirror Descent with Averaging. NIPS Anatoli B. Juditsky, Alexander V. Nazin, Alexandre B. Tsybakov, Nicolas Vayatis
2005	A*	`conf`	Ranking and Scoring Using Empirical Risk Minimization. COLT Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis
2005	J	`jnl`	Recursive Aggregation of Estimators by the Mirror Descent Algorithm with Averaging. Probl. Inf. Transm. Anatoli B. Juditsky, Alexander V. Nazin, Alexandre B. Tsybakov, Nicolas Vayatis
2003	J	`jnl`	On the Rate of Convergence of Regularized Boosting Classifiers. J. Mach. Learn. Res. Gilles Blanchard, Gábor Lugosi, Nicolas Vayatis
2002	A*	`conf`	A Consistent Strategy for Boosting Algorithms. COLT Gábor Lugosi, Nicolas Vayatis
2000	A*	`conf`	The Role of Critical Sets in Vapnik-Chervonenkis Theory. COLT Nicolas Vayatis
1999	—	`conf`	Distribution-Dependent Vapnik-Chervonenkis Bounds. EuroCOLT Nicolas Vayatis, Robert Azencott