Vayatis, Nicolas

45 publications

JMLR 2025 Collaborative Likelihood-Ratio Estimation over Graphs Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos

AISTATS 2025 Collaborative Non-Parametric Two-Sample Testing Alejandro David De Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos

JMLR 2025 Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

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

AISTATS 2025 Stein Boltzmann Sampling: A Variational Approach for Global Optimization Gaëtan Serré, Argyris Kalogeratos, Nicolas Vayatis

AISTATS 2024 Online Non-Parametric Likelihood-Ratio Estimation by Pearson-Divergence Functional Minimization Alejandro D. Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos

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

ECML-PKDD 2022 Fast and Accurate Importance Weighting for Correcting Sample Bias Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

AISTATS 2021 Offline Detection of Change-Points in the Mean for Stationary Graph Signals. Alejandro Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos

NeurIPSW 2021 Handling Distribution Shift in Tire Design Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis

JMLR 2021 Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation Pierre Humbert, Batiste Le Bars, Laurent Oudre, Argyris Kalogeratos, Nicolas Vayatis

ICML 2020 Learning the Piece-Wise Constant Graph Structure of a Varying Ising Model Batiste Le Bars, Pierre Humbert, Argyris Kalogeratos, Nicolas Vayatis

ECML-PKDD 2020 Unsupervised Multi-Source Domain Adaptation for Regression Guillaume Richard, Antoine de Mathelin, Georges Hébrail, Mathilde Mougeot, Nicolas Vayatis

ICML 2018 DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding Thomas Moreau, Laurent Oudre, Nicolas Vayatis

ICML 2017 Global Optimization of Lipschitz Functions Cédric Malherbe, Nicolas Vayatis

ICML 2016 A Ranking Approach to Global Optimization Cedric Malherbe, Emile Contal, Nicolas Vayatis

NeurIPS 2015 Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks Kevin Scaman, Rémi Lemonnier, Nicolas Vayatis

ICML 2014 Gaussian Process Optimization with Mutual Information Emile Contal, Vianney Perchet, Nicolas Vayatis

JMLR 2014 Link Prediction in Graphs with Autoregressive Features Emile Richard, Stéphane Gaïffas, Nicolas Vayatis

ECML-PKDD 2014 Nonparametric Markovian Learning of Triggering Kernels for Mutually Exciting and Mutually Inhibiting Multivariate Hawkes Processes Rémi Lemonnier, Nicolas Vayatis

NeurIPS 2014 Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology Remi Lemonnier, Kevin Scaman, Nicolas Vayatis

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

MLJ 2013 Ranking Data with Ordinal Labels: Optimality and Pairwise Aggregation Stéphan Clémençon, Sylvain Robbiano, Nicolas Vayatis

JMLR 2013 Ranking Forests Stéphan Clémençon, Marine Depecker, Nicolas Vayatis

ALT 2012 Algorithmic Learning Theory - 23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann

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

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

NeurIPS 2012 Link Prediction in Graphs with Autoregressive Features Emile Richard, Stephane Gaiffas, Nicolas Vayatis

MLJ 2011 Adaptive Partitioning Schemes for Bipartite Ranking - How to Grow and Prune a Ranking Tree Stéphan Clémençon, Marine Depecker, Nicolas Vayatis

NeurIPS 2010 Link Discovery Using Graph Feature Tracking Emile Richard, Nicolas Baskiotis, Theodoros Evgeniou, Nicolas Vayatis

NeurIPS 2009 AUC Optimization and the Two-Sample Problem Nicolas Vayatis, Marine Depecker, Stéphan J. Clémençcon

ALT 2009 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

ICML 2009 Nonparametric Estimation of the Precision-Recall Curve Stéphan Clémençon, Nicolas Vayatis

AISTATS 2009 On Partitioning Rules for Bipartite Ranking Stephan Clemencon, Nicolas Vayatis

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

NeurIPS 2008 Empirical Performance Maximization for Linear Rank Statistics Stéphan J. Clémençcon, Nicolas Vayatis

NeurIPS 2008 On Bootstrapping the ROC Curve Patrice Bertail, Stéphan J. Clémençcon, Nicolas Vayatis

NeurIPS 2008 Overlaying Classifiers: A Practical Approach for Optimal Ranking Stéphan J. Clémençcon, Nicolas Vayatis

JMLR 2007 Ranking the Best Instances Stéphan Clémençon, Nicolas Vayatis

NeurIPS 2005 Generalization Error Bounds for Aggregation by Mirror Descent with Averaging Anatoli Juditsky, Alexander Nazin, Alexandre Tsybakov, Nicolas Vayatis

COLT 2005 Ranking and Scoring Using Empirical Risk Minimization Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis

JMLR 2003 On the Rate of Convergence of Regularized Boosting Classifiers Gilles Blanchard, Gábor Lugosi, Nicolas Vayatis

COLT 2002 A Consistent Strategy for Boosting Algorithms Gábor Lugosi, Nicolas Vayatis

COLT 2000 The Role of Critical Sets in Vapnik-Chervonenkis Theory Nicolas Vayatis