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)

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)

Approximate regenerative-block bootstrap for Markov chains. - Patrice Bertail; Stéphan Clémençon (Comput. Stat. Data Anal., 2008)

Adaptive Estimation of the Optimal ROC Curve and a Bipartite Ranking Algorithm. - Stéphan Clémençon; 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)

Kantorovich Distances between Rankings with Applications to Rank Aggregation. - Stéphan Clémençon; Jérémie Jakubowicz (ECML/PKDD (1), 2010)

Hierarchical clustering for graph visualization. - Stéphan Clémençon; Héctor de Arazoza; Fabrice Rossi; Viet-Chi Tran (ESANN, 2011)

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

A Data-Mining Approach to Travel Price Forecasting. - Till Wohlfarth; Stéphan Clémençon; François Roueff; Xavier Casellato (ICMLA (1), 2011)

Visual Mining of Epidemic Networks. - Stéphan Clémençon; Héctor de Arazoza; Fabrice Rossi; Viet-Chi Tran (IWANN (2), 2011)

On U-processes and clustering performance. - Stéphan Clémençon (NIPS, 2011)

Clustering Rankings in the Fourier Domain. - Stéphan Clémençon; Romaric Gaudel; Jérémie Jakubowicz (ECML/PKDD (1), 2011)

Maximising the Quality of Influence. - Charanpal Dhanjal; Stéphan Clémençon (SDM, 2011)

The Evolution of the Cuban HIV/AIDS Network - Charanpal Dhanjal; Stéphan Clémençon; Héctor de Arazoza; Fabrice Rossi; Viet-Chi Tran (CoRR, 2011)

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)

Dissemination of Health Information within Social Networks - Charanpal Dhanjal; Sandrine Blanchemanche; Stéphan Clémençon; Ákos Róna-Tas; Fabrice Rossi (CoRR, 2012)

Scoring anomalies: a M-estimation formulation. - Stéphan Clémençon; Jérémie Jakubowicz (AISTATS, 2013)

On-line learning gossip algorithm in multi-agent systems with local decision rules. - Pascal Bianchi; Stéphan Clémençon; Gemma Morral; Jérémie Jakubowicz (IEEE BigData, 2013)

Maximal Deviations of Incomplete U-statistics with Applications to Empirical Risk Sampling. - Stéphan Clémençon; Sylvain Robbiano; Jessica Tressou (SDM, 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)

Scaling up M-estimation via sampling designs: The Horvitz-Thompson stochastic gradient descent. - Stéphan Clémençon; Patrice Bertail; Emilie Chautru (IEEE BigData, 2014)

Multiresolution analysis of incomplete rankings with applications to prediction. - Eric Sibony; Stéphan Clémençon; Jérémie Jakubowicz (IEEE BigData, 2014)

Anomaly Ranking as Supervised Bipartite Ranking. - Stéphan Clémençon; Sylvain Robbiano (ICML, 2014)

Learning reputation in an authorship network. - Charanpal Dhanjal; Stéphan Clémençon (SAC, 2014)

Online Matrix Completion Through Nuclear Norm Regularisation. - Charanpal Dhanjal; Romaric Gaudel; Stéphan Clémençon (SDM, 2014)

Efficient eigen-updating for spectral graph clustering. - Charanpal Dhanjal; Romaric Gaudel; Stéphan Clémençon (Neurocomputing, 2014)

A statistical view of clustering performance through the theory of U-processes. - Stéphan Clémençon (J. Multivar. Anal., 2014)

Building confidence regions for the ROC surface. - Stéphan Clémençon; Sylvain Robbiano (Pattern Recognit. Lett., 2014)

Collaborative Filtering with Localised Ranking. - Charanpal Dhanjal; Romaric Gaudel; Stéphan Clémençon (AAAI, 2015)

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

Adaptive Sampling for Incremental Optimization Using Stochastic Gradient Descent. - Guillaume Papa; Pascal Bianchi; Stéphan Clémençon (ALT, 2015)

Learning the dependence structure of rare events: a non-asymptotic study. - Nicolas Goix; Anne Sabourin; Stéphan Clémençon (COLT, 2015)

An Ensemble Learning Technique for Multipartite Ranking. - Stéphan Clémençon; Sylvain Robbiano (ESANN, 2015)

MRA-based Statistical Learning from Incomplete Rankings. - Eric Sibony; Stéphan Clémençon; Jérémie Jakubowicz (ICML, 2015)

Scalability of Stochastic Gradient Descent based on "Smart" Sampling Techniques. - Stéphan Clémençon; Aurélien Bellet; Ons Jelassi; Guillaume Papa (INNS Conference on Big Data, 2015)

Extending Gossip Algorithms to Distributed Estimation of U-statistics. - Igor Colin; Aurélien Bellet; Joseph Salmon; Stéphan Clémençon (NIPS, 2015)

SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk. - Guillaume Papa; Stéphan Clémençon; Aurélien Bellet (NIPS, 2015)

AUC Optimisation and Collaborative Filtering. - Charanpal Dhanjal; Romaric Gaudel; Stéphan Clémençon (CoRR, 2015)

A statistical network analysis of the HIV/AIDS epidemics in Cuba. - Stéphan Clémençon; Héctor de Arazoza; Fabrice Rossi; Viet-Chi Tran (Soc. Netw. Anal. Min., 2015)

Extracting Style and Emotion from Handwriting. - Laurence Likforman-Sulem; Anna Esposito; Marcos Faúndez-Zanuy; Stéphan Clémençon (Advances in Neural Networks, 2015)

Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers. - Stéphan Clémençon; Patrice Bertail; Guillaume Papa (ACML, 2016)

Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking. - Nicolas Goix; Anne Sabourin; Stéphan Clémençon (AISTATS, 2016)

Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions. - Igor Colin; Aurélien Bellet; Joseph Salmon; Stéphan Clémençon (ICML, 2016)

On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability. - Guillaume Papa; Aurélien Bellet; Stéphan Clémençon (NIPS, 2016)

Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics. - Stéphan Clémençon; Igor Colin; Aurélien Bellet (J. Mach. Learn. Res., 2016)

An empirical comparison of V-fold penalisation and cross-validation for model selection in distribution-free regression. - Charanpal Dhanjal; Nicolas Baskiotis; Stéphan Clémençon; Nicolas Usunier (Pattern Anal. Appl., 2016)

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 001; Stéphan Clémençon; Alexandre Gramfort; Anne Sabourin (AISTATS, 2017)

Ranking Data with Continuous Labels through Oriented Recursive Partitions. - Stéphan Clémençon; Mastane Achab (NIPS, 2017)

Max K-Armed Bandit: On the ExtremeHunter Algorithm and Beyond. - Mastane Achab; Stéphan Clémençon; Aurélien Garivier; Anne Sabourin; Claire Vernade (ECML/PKDD (2), 2017)

Sparse representation of multivariate extremes with applications to anomaly detection. - Nicolas Goix; Anne Sabourin; Stéphan Clémençon (J. Multivar. Anal., 2017)

EMOTHAW: A Novel Database for Emotional State Recognition From Handwriting and Drawing. - Laurence Likforman-Sulem; Anna Esposito; Marcos Faúndez-Zanuy; Stéphan Clémençon; Gennaro Cordasco (IEEE Trans. Hum. Mach. Syst., 2017)

Profitable Bandits. - Mastane Achab; Stéphan Clémençon; Aurélien Garivier (ACML, 2018)

Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods. - Stéphan Clémençon; François Portier (AISTATS, 2018)

Ranking Median Regression: Learning to Order through Local Consensus. - Stéphan Clémençon; Anna Korba; Eric Sibony (ALT, 2018)

On aggregation in ranking median regression. - Stéphan Clémençon; Anna Korba (ESANN, 2018)

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

Generalization Bounds for Minimum Volume Set Estimation based on Markovian Data. - Patrice Bertail; Gabriela Ciolek; Stéphan Clémençon (ISAIM, 2018)

On Binary Classification in Extreme Regions. - Hamid Jalalzai; Stéphan Clémençon; Anne Sabourin (NeurIPS, 2018)

Functional Isolation Forest. - Guillaume Staerman; Pavlo Mozharovskyi; Stéphan Clémençon; Florence d'Alché-Buc (ACML, 2019)

Autoencoding any Data through Kernel Autoencoders. - Pierre Laforgue; Stéphan Clémençon; Florence d'Alché-Buc (AISTATS, 2019)

Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach. - Mastane Achab; Anna Korba; Stéphan Clémençon (ALT, 2019)

On Medians of (Randomized) Pairwise Means. - Stéphan Clémençon; Pierre Laforgue; Patrice Bertail (ICML, 2019)

On Tree-Based Methods for Similarity Learning. - Stéphan Clémençon; Robin Vogel (LOD, 2019)

Trade-Offs in Large-Scale Distributed Tuplewise Estimation And Learning. - Robin Vogel; Aurélien Bellet; Stéphan Clémençon; Ons Jelassi; Guillaume Papa (ECML/PKDD (2), 2019)

A LSTM Approach to Detection of Autonomous Vehicle Hijacking. - Naman Singh Negi; Ons Jelassi; Stéphan Clémençon; Sebastian Fischmeister (VEHITS, 2019)

Traffic Analysis Based on Bluetooth Passive Scanning. - Safa Boudabous; Julian Garbiso; Bertrand Leroy; Stéphan Clémençon; Houda Labiod (VTC Spring, 2019)

Empirical Risk Minimization under Random Censorship: Theory and Practice. - Guillaume Ausset; Stéphan Clémençon; François Portier (CoRR, 2019)

Statistical Learning from Biased Training Samples. - Pierre Laforgue; Stéphan Clémençon (CoRR, 2019)

The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth measure. - Guillaume Staerman; Pavlo Mozharovskyi; Stéphan Clémençon (AISTATS, 2020)

A Multiclass Classification Approach to Label Ranking. - Robin Vogel; Stéphan Clémençon (AISTATS, 2020)

Identifying the "right" level of explanation in a given situation. - Valérie Beaudouin; Isabelle Bloch; David Bounie; Stéphan Clémençon; Florence d'Alché-Buc; James Eagan; Winston Maxwell; Pavlo Mozharovskyi; Jayneel Parekh (NeHuAI@ECAI, 2020)

Weighted Emprirical Risk Minimization: Transfer Learning based on Importance Sampling. - Robin Vogel; Mastane Achab; Stéphan Clémençon; Charles Tillier (ESANN, 2020)

Distributed online Data Anomaly Detection for connected vehicles. - Naman Singh Negi; Ons Jelassi; Hakima Chaouchi; Stéphan Clémençon (ICAIIC, 2020)

Percolation-Based Detection of Anomalous Subgraphs in Complex Networks. - Corentin Larroche; Johan Mazel; Stéphan Clémençon (IDA, 2020)

Statistical learning based on Markovian data maximal deviation inequalities and learning rates. - Stéphan Clémençon; Patrice Bertail; Gabriela Ciolek (Ann. Math. Artif. Intell., 2020)

Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling. - Robin Vogel; Mastane Achab; Stéphan Clémençon; Charles Tillier (CoRR, 2020)

Learning Fair Scoring Functions: Fairness Definitions, Algorithms and Generalization Bounds for Bipartite Ranking. - Robin Vogel; Aurélien Bellet; Stéphan Clémençon (CoRR, 2020)

Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach. - Valérie Beaudouin; Isabelle Bloch; David Bounie; Stéphan Clémençon; Florence d'Alché-Buc; James Eagan; Winston Maxwell; Pavlo Mozharovskyi; Jayneel Parekh (CoRR, 2020)

How Robust is the Median-of-Means? Concentration Bounds in Presence of Outliers. - Pierre Laforgue; Guillaume Staerman; Stéphan Clémençon (CoRR, 2020)

A multivariate extreme value theory approach to anomaly clustering and visualization. - Maël Chiapino; Stéphan Clémençon; Vincent Feuillard; Anne Sabourin (Comput. Stat., 2020)

Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications. - Guillaume Ausset; Stéphan Clémençon; François Portier (AISTATS, 2021)

Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints. - Robin Vogel; Aurélien Bellet; Stéphan Clémençon (AISTATS, 2021)

Individual Survival Curves with Conditional Normalizing Flows. - Guillaume Ausset; Tom Ciffreo; François Portier; Stéphan Clémençon; Timothée Papin (DSAA, 2021)

Dynamic Graph Convolutional LSTM application for traffic flow estimation from error-prone measurements: results and transferability analysis. - Safa Boudabous; Stéphan Clémençon; Houda Labiod; Julian Garbiso (DSAA, 2021)

Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing. - Corentin Larroche; Johan Mazel; Stéphan Clémençon (ESANN, 2021)

Learning from Biased Data: A Semi-Parametric Approach. - Patrice Bertail; Stéphan Clémençon; Yannick Guyonvarch; Nathan Noiry (ICML, 2021)

Generalization Bounds in the Presence of Outliers: a Median-of-Means Study. - Pierre Laforgue; Guillaume Staerman; Stéphan Clémençon (ICML, 2021)

Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics. - Myrto Limnios; Nathan Noiry; Stéphan Clémençon (LIDTA@ECML/PKDD, 2021)

Depth-based pseudo-metrics between probability distributions. - Guillaume Staerman; Pavlo Mozharovskyi; Stéphan Clémençon; Florence d'Alché-Buc (CoRR, 2021)

Dynamically Modelling Heterogeneous Higher-Order Interactions for Malicious Behavior Detection in Event Logs. - Corentin Larroche; Johan Mazel; Stéphan Clémençon (CoRR, 2021)

Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis. - Guillaume Staerman; Pavlo Mozharovskyi; Stéphan Clémençon (CoRR, 2021)

Visual Recognition with Deep Learning from Biased Image Datasets. - Robin Vogel; Stéphan Clémençon; Pierre Laforgue (CoRR, 2021)

Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications. - Morgane Goibert; Stéphan Clémençon; Ekhine Irurozki; Pavlo Mozharovskyi (AISTATS, 2022)

Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model. - Jean-Rémy Conti; Nathan Noiry; Stéphan Clémençon; Vincent Despiegel; Stéphane Gentric (ICML, 2022)

What are the best Systems? New Perspectives on NLP Benchmarking. - Pierre Colombo; Nathan Noiry; Ekhine Irurozki; Stéphan Clémençon (NeurIPS, 2022)

Improving the quality control of seismic data through active learning. - Mathieu Chambefort; Raphaël Butez; Emilie Chautru; Stéphan Clémençon (CoRR, 2022)

A Statistical Learning View of Simple Kriging. - Emilia Siviero; Emilie Chautru; Stéphan Clémençon (CoRR, 2022)

EMOTHAW: A novel database for emotional state recognition from handwriting. - Laurence Likforman-Sulem; Anna Esposito; Marcos Faúndez-Zanuy; Stéphan Clémençon; Gennaro Cordasco (CoRR, 2022)

Assessing Performance and Fairness Metrics in Face Recognition - Bootstrap Methods. - Jean-Rémy Conti; Stéphan Clémençon (CoRR, 2022)

Empirical Risk Minimization under Random Censorship. - Guillaume Ausset; Stéphan Clémençon; François Portier (J. Mach. Learn. Res., 2022)

Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues. - Morgane Goibert; Clément Calauzènes; Ekhine Irurozki; Stéphan Clémençon (ICML, 2023)

Active Bipartite Ranking. - James Cheshire; Vincent Laurent; Stéphan Clémençon (NeurIPS, 2023)

Fast and accurate nonlinear interference in-band spectrum prediction for sparse channel allocation. - Isaia Andrenacci; Matteo Lonardi; Petros Ramantanis; Elie Awwad; Ekhiñe Irurozki; Stéphan Clémençon (ONDM, 2023)

On Regression in Extreme Regions. - Nathan Huet; Stéphan Clémençon; Anne Sabourin (CoRR, 2023)

Functional anomaly detection: a benchmark study. - Guillaume Staerman; Eric Adjakossa; Pavlo Mozharovskyi; Vera Hofer; Jayant Sen Gupta; Stéphan Clémençon (Int. J. Data Sci. Anal., 2023)

On Ranking-based Tests of Independence. - Myrto Limnios; Stéphan Clémençon (AISTATS, 2024)

Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks. - Anas Himmi; Ekhine Irurozki; Nathan Noiry; Stéphan Clémençon; Pierre Colombo (EMNLP (Findings), 2024)

Human Pose Estimation Based Biomechanical Feature Extraction for Long Jumps. - Qi Gan; Sao Mai Nguyen; Mounîm A. El-Yacoubi; Eric Fenaux; Stéphan Clémençon (HSI, 2024)

Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition. - Jean-Rémy Conti; Stéphan Clémençon (ICLR, 2024)

Mitigating Bias in Facial Recognition Systems: Centroid Fairness Loss Optimization. - Jean-Rémy Conti; Stéphan Clémençon (ICPR (Workshops and Challenges, 1), 2024)

Machine Learning-Driven Low-Complexity Optical Power Optimization for Point-to-Point Links. - Isaia Andrenacci; Matteo Lonardi; Petros Ramantanis; Elie Awwad; Ekhiñe Irurozki; Stéphan Clémençon; Paolo Serena; Chiara Lasagni; Sébastien Bigo; Patricia Layec (OFC, 2024)

Flexible Parametric Inference for Space-Time Hawkes Processes. - Emilia Siviero; Guillaume Staerman; Stéphan Clémençon; Thomas Moreau 001 (CoRR, 2024)

Machine-learning-based technique to establish ASE or Kerr impairment dominance in optical transmission. - Isaia Andrenacci; Matteo Lonardi; Petros Ramantanis; Elie Awwad; Ekhiñe Irurozki; Stéphan Clémençon; Sylvain Almonacil (J. Opt. Commun. Netw., 2024)

A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions. - Guillaume Staerman; Pavlo Mozharovskyi; Pierre Colombo; Stéphan Clémençon; Florence d'Alché-Buc (Trans. Mach. Learn. Res., 2024)

Learning to rank anomalies: scalar performance criteria and maximization of rank statistics. - Myrto Limnios; Nathan Noiry; Stéphan Clémençon (Mach. Learn., 2024)

Weibull mixture estimation based on censored data with applications to clustering in reliability engineering. - Florian Lamalle; Vincent Feuillard; Anne Sabourin; Stéphan Clémençon (Qual. Reliab. Eng. Int., 2024)

Active Bipartite Ranking with Smooth Posterior Distributions. - James Cheshire; Stéphan Clémençon (AISTATS, 2025)

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

Robust Distributed Estimation: Extending Gossip Algorithms to Ranking and Trimmed Means. - Anna Van Elst; Igor Colin; Stéphan Clémençon (CoRR, 2025)

Feature Impact Analysis on Top Long-Jump Performances with Quantile Random Forest and Explainable AI Techniques. - Qi Gan; Stéphan Clémençon; Mounîm A. El-Yacoubi; Sao Mai Nguyen; Eric Fenaux; Ons Jelassi (CoRR, 2025)