Clémençon, Stéphan

59 publications

AISTATS 2025 Active Bipartite Ranking with Smooth Posterior Distributions James Cheshire, Stephan Clémençon

CVPRW 2025 Polar Coordinate-Based 2D Pose Prior with Neural Distance Field Qi Gan, Sao Mai Nguyen, Eric Fenaux, Stéphan Clémençon, Mounim A. El-Yacoubi

NeurIPS 2025 Robust Distributed Estimation: Extending Gossip Algorithms to Ranking and Trimmed Means Anna van Elst, Igor Colin, Stephan Clémençon

TMLR 2024 A Pseudo-Metric Between Probability Distributions Based on Depth-Trimmed Regions Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo, Stephan Clémençon, Florence d'Alché-Buc

ICLR 2024 Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition Jean-Rémy Conti, Stephan Clémençon

MLJ 2024 Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Rank Statistics Myrto Limnios, Nathan Noiry, Stéphan Clémençon

NeurIPSW 2024 Mitigating Bias in Facial Recognition Systems: Centroid Fairness Loss Optimization Jean-Rémy Conti, Stephan Clémençon

AISTATS 2024 On Ranking-Based Tests of Independence Myrto Limnios, Stéphan Clémençon

NeurIPS 2023 Active Bipartite Ranking James Cheshire, Vincent Laurent, Stephan Clémençon

ICML 2023 Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues Morgane Goibert, Clément Calauzènes, Ekhine Irurozki, Stephan Clémençon

AISTATS 2022 Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications Morgane Goibert, Stephan Clemencon, Ekhine Irurozki, Pavlo Mozharovskyi

NeurIPSW 2022 Assessing Performance and Fairness Metrics in Face Recognition - Bootstrap Methods Jean-Rémy Conti, Stephan Clémençon

JMLR 2022 Empirical Risk Minimization Under Random Censorship Guillaume Ausset, Stephan Clémençon, François Portier

ICML 2022 Mitigating Gender Bias in Face Recognition Using the Von Mises-Fisher Mixture Model Jean-Rémy Conti, Nathan Noiry, Stephan Clemencon, Vincent Despiegel, Stéphane Gentric

NeurIPS 2022 What Are the Best Systems? New Perspectives on NLP Benchmarking Pierre Colombo, Nathan Noiry, Ekhine Irurozki, Stephan Clémençon

AISTATS 2021 Learning Fair Scoring Functions: Bipartite Ranking Under ROC-Based Fairness Constraints Robin Vogel, Aurélien Bellet, Stephan Clémençon

AISTATS 2021 Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications Guillaume Ausset, Stephan Clémencon, François Portier

ICML 2021 Generalization Bounds in the Presence of Outliers: A Median-of-Means Study Pierre Laforgue, Guillaume Staerman, Stephan Clémençon

ICML 2021 Learning from Biased Data: A Semi-Parametric Approach Patrice Bertail, Stephan Clémençon, Yannick Guyonvarch, Nathan Noiry

AISTATS 2019 Autoencoding Any Data Through Kernel Autoencoders Pierre Laforgue, Stéphan Clémençon, Florence d’Alche-Buc

ALT 2019 Dimensionality Reduction and (Bucket) Ranking: A Mass Transportation Approach Mastane Achab, Anna Korba, Stephan Clémençon

ACML 2019 Functional Isolation Forest Guillaume Staerman, Pavlo Mozharovskyi, Stephan Clémençon, Florence d’Alché-Buc

ICML 2019 On Medians of (Randomized) Pairwise Means Pierre Laforgue, Stephan Clemencon, Patrice Bertail

ECML-PKDD 2019 Trade-Offs in Large-Scale Distributed Tuplewise Estimation and Learning Robin Vogel, Aurélien Bellet, Stéphan Clémençon, Ons Jelassi, Guillaume Papa

ICML 2018 A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization Robin Vogel, Aurélien Bellet, Stéphan Clémençon

AISTATS 2018 Beating Monte Carlo Integration: A Nonasymptotic Study of Kernel Smoothing Methods Stéphan Clémençon, François Portier

NeurIPS 2018 On Binary Classification in Extreme Regions Hamid Jalalzai, Stephan Clémençon, Anne Sabourin

ACML 2018 Profitable Bandits Mastane Achab, Stephan Clémençon, Aurélien Garivier

ALT 2018 Ranking Median Regression: Learning to Order Through Local Consensus Stephan Clémençon, Anna Korba, Eric Sibony

AISTATS 2017 A Learning Theory of Ranking Aggregation Anna Korba, Stéphan Clémençon, Eric Sibony

AISTATS 2017 Anomaly Detection in Extreme Regions via Empirical MV-Sets on the Sphere Albert Thomas, Stéphan Clémençon, Alexandre Gramfort, Anne Sabourin

ECML-PKDD 2017 Max K-Armed Bandit: On the ExtremeHunter Algorithm and Beyond Mastane Achab, Stéphan Clémençon, Aurélien Garivier, Anne Sabourin, Claire Vernade

NeurIPS 2017 Ranking Data with Continuous Labels Through Oriented Recursive Partitions Stéphan Clémençon, Mastane Achab

ICML 2016 Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions Igor Colin, Aurelien Bellet, Joseph Salmon, Stéphan Clémençon

ACML 2016 Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers Stephan Clemencon, Patrice Bertail, Guillaume Papa

NeurIPS 2016 On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability Guillaume Papa, Aurélien Bellet, Stephan Clémençon

JMLR 2016 Scaling-up Empirical Risk Minimization: Optimization of Incomplete $u$-Statistics Stephan Clémençon, Igor Colin, Aurélien Bellet

AISTATS 2016 Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking Nicolas Goix, Anne Sabourin, Stéphan Clémençon

ALT 2015 Adaptive Sampling for Incremental Optimization Using Stochastic Gradient Descent Guillaume Papa, Pascal Bianchi, Stéphan Clémençon

AAAI 2015 Collaborative Filtering with Localised Ranking Charanpal Dhanjal, Romaric Gaudel, Stéphan Clémençon

NeurIPS 2015 Extending Gossip Algorithms to Distributed Estimation of U-Statistics Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon

COLT 2015 Learning the Dependence Structure of Rare Events: A Non-Asymptotic Study Nicolas Goix, Anne Sabourin, Stéphan Clémençon

ICML 2015 MRA-Based Statistical Learning from Incomplete Rankings Eric Sibony, Stéphan Clemençon, Jérémie Jakubowicz

AISTATS 2015 On Anomaly Ranking and Excess-Mass Curves Nicolas Goix, Anne Sabourin, Stéphan Clémençon

NeurIPS 2015 SGD Algorithms Based on Incomplete U-Statistics: Large-Scale Minimization of Empirical Risk Guillaume Papa, Stéphan Clémençon, Aurélien Bellet

ICML 2014 Anomaly Ranking as Supervised Bipartite Ranking Stephan Clémençon, Sylvain Robbiano

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

AISTATS 2013 Scoring Anomalies: A M-Estimation Formulation Stéphan Clémençon, Jérémie Jakubowicz

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

ECML-PKDD 2011 Clustering Rankings in the Fourier Domain Stéphan Clémençon, Romaric Gaudel, Jérémie Jakubowicz

ICML 2011 Minimax Learning Rates for Bipartite Ranking and Plug-in Rules Sylvain Robbiano, Stéphan Clémençon

ECML-PKDD 2010 Kantorovich Distances Between Rankings with Applications to Rank Aggregation Stéphan Clémençon, Jérémie Jakubowicz

ALT 2009 Adaptive Estimation of the Optimal ROC Curve and a Bipartite Ranking Algorithm Stéphan Clémençon, 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

JMLR 2007 Ranking the Best Instances Stéphan Clémençon, Nicolas Vayatis

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