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ORCIDStéphan Clémençon
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- affiliation: Telecom Paris, Palaiseau, France
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2020 – today
- 2024
[j23]Myrto Limnios
, Nathan Noiry, Stéphan Clémençon:
Learning to rank anomalies: scalar performance criteria and maximization of rank statistics. Mach. Learn. 113(11): 8623-8653 (2024)- [j22]Florian Lamalle, Vincent Feuillard, Anne Sabourin, Stéphan Clémençon:
Weibull mixture estimation based on censored data with applications to clustering in reliability engineering. Qual. Reliab. Eng. Int. 40(8): 4247-4261 (2024) - [j21]Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo, Stéphan Clémençon, Florence d'Alché-Buc:
A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions. Trans. Mach. Learn. Res. 2024 (2024) - [c85]Myrto Limnios, Stéphan Clémençon:
On Ranking-based Tests of Independence. AISTATS 2024: 577-585 - [c84]Anas Himmi, Ekhine Irurozki, Nathan Noiry, Stéphan Clémençon, Pierre Colombo:
Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks. EMNLP (Findings) 2024: 11759-11785 - [c83]Qi Gan, Sao Mai Nguyen, Mounîm A. El-Yacoubi, Eric Fenaux, Stéphan Clémençon:
Human Pose Estimation Based Biomechanical Feature Extraction for Long Jumps. HSI 2024: 1-6 - [c82]Jean-Rémy Conti, Stéphan Clémençon:
Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition. ICLR 2024 - [c81]Isaia Andrenacci, Matteo Lonardi, Petros Ramantanis, Elie Awwad, Ekhiñe Irurozki, Stéphan Clémençon, Paolo Serena, Chiara Lasagni, Sébastien Bigo, Patricia Layec:
Machine Learning-Driven Low-Complexity Optical Power Optimization for Point-to-Point Links. OFC 2024: 1-3 - [i45]Emilia Siviero, Guillaume Staerman, Stéphan Clémençon, Thomas Moreau:
Flexible Parametric Inference for Space-Time Hawkes Processes. CoRR abs/2406.06849 (2024) - 2023
- [j20]Guillaume Staerman, Eric Adjakossa, Pavlo Mozharovskyi, Vera Hofer, Jayant Sen Gupta, Stéphan Clémençon:
Functional anomaly detection: a benchmark study. Int. J. Data Sci. Anal. 16(1): 101-117 (2023) - [c80]Morgane Goibert, Clément Calauzènes, Ekhine Irurozki, Stéphan Clémençon:
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues. ICML 2023: 11584-11597 - [c79]James Cheshire, Vincent Laurent, Stéphan Clémençon:
Active Bipartite Ranking. NeurIPS 2023 - [c78]Isaia Andrenacci, Matteo Lonardi, Petros Ramantanis, Elie Awwad, Ekhiñe Irurozki, Stéphan Clémençon:
Fast and accurate nonlinear interference in-band spectrum prediction for sparse channel allocation. ONDM 2023: 1-5 - [i44]Nathan Huet, Stéphan Clémençon, Anne Sabourin:
On Regression in Extreme Regions. CoRR abs/2303.03084 (2023) - [i43]Morgane Goibert, Clément Calauzènes, Ekhine Irurozki, Stéphan Clémençon:
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues. CoRR abs/2303.12878 (2023) - [i42]Anas Himmi, Ekhine Irurozki, Nathan Noiry, Stéphan Clémençon, Pierre Colombo:
Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks. CoRR abs/2305.10284 (2023) - 2022
- [j19]Guillaume Ausset, Stéphan Clémençon, François Portier:
Empirical Risk Minimization under Random Censorship. J. Mach. Learn. Res. 23: 5:1-5:59 (2022) - [c77]Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki, Pavlo Mozharovskyi:
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications. AISTATS 2022: 10376-10406 - [c76]Jean-Rémy Conti, Nathan Noiry, Stéphan Clémençon, Vincent Despiegel, Stéphane Gentric:
Mitigating Gender Bias in Face Recognition using the von Mises-Fisher Mixture Model. ICML 2022: 4344-4369 - [c75]Pierre Colombo, Nathan Noiry, Ekhine Irurozki, Stéphan Clémençon:
What are the best Systems? New Perspectives on NLP Benchmarking. NeurIPS 2022 - [i41]Guillaume Staerman, Eric Adjakossa, Pavlo Mozharovskyi, Vera Hofer, Jayant Sen Gupta, Stéphan Clémençon:
Functional Anomaly Detection: a Benchmark Study. CoRR abs/2201.05115 (2022) - [i40]Mathieu Chambefort, Raphaël Butez, Emilie Chautru, Stéphan Clémençon:
Improving the quality control of seismic data through active learning. CoRR abs/2201.06616 (2022) - [i39]Morgane Goibert, Stéphan Clémençon, Ekhine Irurozki, Pavlo Mozharovskyi:
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications. CoRR abs/2201.08105 (2022) - [i38]Pierre Colombo, Nathan Noiry, Ekhine Irurozki, Stéphan Clémençon:
What are the best systems? New perspectives on NLP Benchmarking. CoRR abs/2202.03799 (2022) - [i37]Emilia Siviero, Emilie Chautru, Stéphan Clémençon:
A Statistical Learning View of Simple Kriging. CoRR abs/2202.07365 (2022) - [i36]Laurence Likforman-Sulem, Anna Esposito, Marcos Faúndez-Zanuy, Stéphan Clémençon, Gennaro Cordasco:
EMOTHAW: A novel database for emotional state recognition from handwriting. CoRR abs/2202.12245 (2022) - [i35]Jean-Rémy Conti, Nathan Noiry, Vincent Despiegel, Stéphane Gentric, Stéphan Clémençon:
Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model. CoRR abs/2210.13664 (2022) - [i34]Pierre Laforgue, Stéphan Clémençon, Patrice Bertail:
On Medians of (Randomized) Pairwise Means. CoRR abs/2211.00603 (2022) - [i33]Jean-Rémy Conti, Stéphan Clémençon:
Assessing Performance and Fairness Metrics in Face Recognition - Bootstrap Methods. CoRR abs/2211.07245 (2022) - 2021
- [c74]Guillaume Ausset, Stéphan Clémençon, François Portier:
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications. AISTATS 2021: 532-540 - [c73]Robin Vogel, Aurélien Bellet, Stéphan Clémençon:
Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints. AISTATS 2021: 784-792 - [c72]Guillaume Ausset, Tom Ciffreo, François Portier, Stéphan Clémençon, Timothée Papin:
Individual Survival Curves with Conditional Normalizing Flows. DSAA 2021: 1-10 - [c71]Safa Boudabous, Stéphan Clémençon, Houda Labiod, Julian Garbiso:
Dynamic Graph Convolutional LSTM application for traffic flow estimation from error-prone measurements: results and transferability analysis. DSAA 2021: 1-10 - [c70]Safa Boudabous, Stéphan Clémençon, Houda Labiod, Julian Garbiso:
Dynamic Graph Convolutional LSTM application for traffic flow estimation from error-prone measurements: results and transferability analysis. DSAA 2021: 1-10 - [c69]Corentin Larroche, Johan Mazel, Stéphan Clémençon:
Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing. ESANN 2021 - [c68]Patrice Bertail, Stéphan Clémençon, Yannick Guyonvarch, Nathan Noiry:
Learning from Biased Data: A Semi-Parametric Approach. ICML 2021: 803-812 - [c67]Pierre Laforgue, Guillaume Staerman, Stéphan Clémençon:
Generalization Bounds in the Presence of Outliers: a Median-of-Means Study. ICML 2021: 5937-5947 - [c66]Myrto Limnios, Nathan Noiry, Stéphan Clémençon:
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics. LIDTA@ECML/PKDD 2021: 63-75 - [i32]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc:
Depth-based pseudo-metrics between probability distributions. CoRR abs/2103.12711 (2021) - [i31]Corentin Larroche, Johan Mazel, Stéphan Clémençon:
Dynamically Modelling Heterogeneous Higher-Order Interactions for Malicious Behavior Detection in Event Logs. CoRR abs/2103.15708 (2021) - [i30]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon:
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis. CoRR abs/2106.11068 (2021) - [i29]Guillaume Ausset, Tom Ciffreo, François Portier, Stéphan Clémençon, Timothée Papin:
Individual Survival Curves with Conditional Normalizing Flows. CoRR abs/2107.12825 (2021) - [i28]Robin Vogel, Stéphan Clémençon, Pierre Laforgue:
Visual Recognition with Deep Learning from Biased Image Datasets. CoRR abs/2109.02357 (2021) - 2020
- [j18]Stéphan Clémençon, Patrice Bertail, Gabriela Ciolek:
Statistical learning based on Markovian data maximal deviation inequalities and learning rates. Ann. Math. Artif. Intell. 88(7): 735-757 (2020) - [j17]Maël Chiapino, Stéphan Clémençon, Vincent Feuillard, Anne Sabourin:
A multivariate extreme value theory approach to anomaly clustering and visualization. Comput. Stat. 35(2): 607-628 (2020) - [c65]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon:
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth measure. AISTATS 2020: 570-579 - [c64]Robin Vogel, Stéphan Clémençon:
A Multiclass Classification Approach to Label Ranking. AISTATS 2020: 1421-1430 - [c63]Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh:
Identifying the "right" level of explanation in a given situation. NeHuAI@ECAI 2020: 63-66 - [c62]Robin Vogel, Mastane Achab, Stéphan Clémençon, Charles Tillier:
Weighted Emprirical Risk Minimization: Transfer Learning based on Importance Sampling. ESANN 2020: 515-520 - [c61]Naman Singh Negi, Ons Jelassi, Hakima Chaouchi, Stéphan Clémençon:
Distributed online Data Anomaly Detection for connected vehicles. ICAIIC 2020: 494-500 - [c60]Corentin Larroche, Johan Mazel, Stéphan Clémençon:
Percolation-Based Detection of Anomalous Subgraphs in Complex Networks. IDA 2020: 287-299 - [i27]Robin Vogel, Mastane Achab, Stéphan Clémençon, Charles Tillier:
Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling. CoRR abs/2002.05145 (2020) - [i26]Robin Vogel, Aurélien Bellet, Stéphan Clémençon:
Learning Fair Scoring Functions: Fairness Definitions, Algorithms and Generalization Bounds for Bipartite Ranking. CoRR abs/2002.08159 (2020) - [i25]Stéphan Clémençon, Robin Vogel:
A Multiclass Classification Approach to Label Ranking. CoRR abs/2002.09420 (2020) - [i24]Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d'Alché-Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, Jayneel Parekh:
Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach. CoRR abs/2003.07703 (2020) - [i23]Pierre Laforgue, Guillaume Staerman, Stéphan Clémençon:
How Robust is the Median-of-Means? Concentration Bounds in Presence of Outliers. CoRR abs/2006.05240 (2020) - [i22]Guillaume Ausset, Stéphan Clémençon, François Portier:
Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications. CoRR abs/2006.15043 (2020)
2010 – 2019
- 2019
- [c59]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc:
Functional Isolation Forest. ACML 2019: 332-347 - [c58]Pierre Laforgue, Stéphan Clémençon, Florence d'Alché-Buc:
Autoencoding any Data through Kernel Autoencoders. AISTATS 2019: 1061-1069 - [c57]Mastane Achab, Anna Korba, Stéphan Clémençon:
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach. ALT 2019: 64-93 - [c56]Stéphan Clémençon, Pierre Laforgue, Patrice Bertail:
On Medians of (Randomized) Pairwise Means. ICML 2019: 1272-1281 - [c55]Stéphan Clémençon, Robin Vogel:
On Tree-Based Methods for Similarity Learning. LOD 2019: 676-688 - [c54]Robin Vogel, Aurélien Bellet, Stéphan Clémençon, Ons Jelassi, Guillaume Papa:
Trade-Offs in Large-Scale Distributed Tuplewise Estimation And Learning. ECML/PKDD (2) 2019: 229-245 - [c53]Naman Singh Negi, Ons Jelassi, Stéphan Clémençon, Sebastian Fischmeister:
A LSTM Approach to Detection of Autonomous Vehicle Hijacking. VEHITS 2019: 475-482 - [c52]Safa Boudabous, Julian Garbiso, Bertrand Leroy, Stéphan Clémençon, Houda Labiod:
Traffic Analysis Based on Bluetooth Passive Scanning. VTC Spring 2019: 1-6 - [i21]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon, Florence d'Alché-Buc:
Functional Isolation Forest. CoRR abs/1904.04573 (2019) - [i20]Guillaume Ausset, Stéphan Clémençon, François Portier:
Empirical Risk Minimization under Random Censorship: Theory and Practice. CoRR abs/1906.01908 (2019) - [i19]Robin Vogel, Aurélien Bellet, Stéphan Clémençon, Ons Jelassi, Guillaume Papa:
Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning. CoRR abs/1906.09234 (2019) - [i18]Stéphan Clémençon, Robin Vogel:
On Tree-based Methods for Similarity Learning. CoRR abs/1906.09243 (2019) - [i17]Pierre Laforgue, Stéphan Clémençon:
Statistical Learning from Biased Training Samples. CoRR abs/1906.12304 (2019) - [i16]Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon:
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure. CoRR abs/1910.04085 (2019) - 2018
- [c51]Mastane Achab, Stéphan Clémençon, Aurélien Garivier:
Profitable Bandits. ACML 2018: 694-709 - [c50]Stéphan Clémençon, François Portier:
Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods. AISTATS 2018: 548-556 - [c49]Stéphan Clémençon, Anna Korba, Eric Sibony:
Ranking Median Regression: Learning to Order through Local Consensus. ALT 2018: 212-245 - [c48]Stéphan Clémençon, Anna Korba:
On aggregation in ranking median regression. ESANN 2018 - [c47]Robin Vogel, Aurélien Bellet, Stéphan Clémençon:
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization. ICML 2018: 5062-5071 - [c46]Patrice Bertail, Gabriela Ciolek, Stéphan Clémençon:
Generalization Bounds for Minimum Volume Set Estimation based on Markovian Data. ISAIM 2018 - [c45]Hamid Jalalzai, Stéphan Clémençon, Anne Sabourin:
On Binary Classification in Extreme Regions. NeurIPS 2018: 3096-3104 - [i15]Mastane Achab, Stéphan Clémençon, Aurélien Garivier:
Profitable Bandits. CoRR abs/1805.02908 (2018) - [i14]Pierre Laforgue, Stéphan Clémençon, Florence d'Alché-Buc:
Autoencoding any Data through Kernel Autoencoders. CoRR abs/1805.11028 (2018) - [i13]Robin Vogel, Aurélien Bellet, Stéphan Clémençon:
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization. CoRR abs/1807.06981 (2018) - [i12]Mastane Achab, Anna Korba, Stéphan Clémençon:
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach. CoRR abs/1810.06291 (2018) - 2017
- [j16]Nicolas Goix, Anne Sabourin, Stéphan Clémençon:
Sparse representation of multivariate extremes with applications to anomaly detection. J. Multivar. Anal. 161: 12-31 (2017) - [j15]Laurence Likforman-Sulem, Anna Esposito, Marcos Faúndez-Zanuy, Stéphan Clémençon, Gennaro Cordasco:
EMOTHAW: A Novel Database for Emotional State Recognition From Handwriting and Drawing. IEEE Trans. Hum. Mach. Syst. 47(2): 273-284 (2017) - [c44]Anna Korba, Stéphan Clémençon, Eric Sibony:
A Learning Theory of Ranking Aggregation. AISTATS 2017: 1001-1010 - [c43]Albert Thomas, Stéphan Clémençon, Alexandre Gramfort, Anne Sabourin:
Anomaly Detection in Extreme Regions via Empirical MV-sets on the Sphere. AISTATS 2017: 1011-1019 - [c42]Stéphan Clémençon, Mastane Achab:
Ranking Data with Continuous Labels through Oriented Recursive Partitions. NIPS 2017: 4600-4608 - [c41]Mastane Achab, Stéphan Clémençon, Aurélien Garivier, Anne Sabourin, Claire Vernade:
Max K-Armed Bandit: On the ExtremeHunter Algorithm and Beyond. ECML/PKDD (2) 2017: 389-404 - [i11]Mastane Achab, Stéphan Clémençon, Aurélien Garivier, Anne Sabourin, Claire Vernade:
Max K-armed bandit: On the ExtremeHunter algorithm and beyond. CoRR abs/1707.08820 (2017) - 2016
- [j14]Stéphan Clémençon, Igor Colin, Aurélien Bellet:
Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics. J. Mach. Learn. Res. 17: 76:1-76:36 (2016) - [j13]Charanpal Dhanjal, Nicolas Baskiotis, Stéphan Clémençon, Nicolas Usunier:
An empirical comparison of V-fold penalisation and cross-validation for model selection in distribution-free regression. Pattern Anal. Appl. 19(1): 41-53 (2016) - [c40]Stéphan Clémençon, Patrice Bertail, Guillaume Papa:
Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers. ACML 2016: 142-157 - [c39]Nicolas Goix, Anne Sabourin, Stéphan Clémençon:
Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking. AISTATS 2016: 75-83 - [c38]Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon:
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions. ICML 2016: 1388-1396 - [c37]Guillaume Papa, Aurélien Bellet, Stéphan Clémençon:
On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability. NIPS 2016: 694-702 - [i10]Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon:
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions. CoRR abs/1606.02421 (2016) - 2015
- [j12]Stéphan Clémençon, Héctor de Arazoza, Fabrice Rossi, Viet-Chi Tran:
A statistical network analysis of the HIV/AIDS epidemics in Cuba. Soc. Netw. Anal. Min. 5(1): 58:1-58:14 (2015) - [c36]Charanpal Dhanjal, Romaric Gaudel, Stéphan Clémençon:
Collaborative Filtering with Localised Ranking. AAAI 2015: 2554-2560 - [c35]Nicolas Goix, Anne Sabourin, Stéphan Clémençon:
On Anomaly Ranking and Excess-Mass Curves. AISTATS 2015 - [c34]Guillaume Papa, Pascal Bianchi, Stéphan Clémençon:
Adaptive Sampling for Incremental Optimization Using Stochastic Gradient Descent. ALT 2015: 317-331 - [c33]Nicolas Goix, Anne Sabourin, Stéphan Clémençon:
Learning the dependence structure of rare events: a non-asymptotic study. COLT 2015: 843-860 - [c32]Stéphan Clémençon, Sylvain Robbiano:
An Ensemble Learning Technique for Multipartite Ranking. ESANN 2015 - [c31]Eric Sibony, Stéphan Clémençon, Jérémie Jakubowicz:
MRA-based Statistical Learning from Incomplete Rankings. ICML 2015: 1432-1441 - [c30]Stéphan Clémençon, Aurélien Bellet, Ons Jelassi, Guillaume Papa:
Scalability of Stochastic Gradient Descent based on "Smart" Sampling Techniques. INNS Conference on Big Data 2015: 308-315 - [c29]Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon:
Extending Gossip Algorithms to Distributed Estimation of U-statistics. NIPS 2015: 271-279 - [c28]Guillaume Papa, Stéphan Clémençon, Aurélien Bellet:
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk. NIPS 2015: 1027-1035 - [p1]Laurence Likforman-Sulem, Anna Esposito, Marcos Faúndez-Zanuy, Stéphan Clémençon:
Extracting Style and Emotion from Handwriting. Advances in Neural Networks 2015: 347-355 - [i9]Stéphan Clémençon, Aurélien Bellet, Igor Colin:
Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics. CoRR abs/1501.02629 (2015) - [i8]Charanpal Dhanjal, Romaric Gaudel, Stéphan Clémençon:
AUC Optimisation and Collaborative Filtering. CoRR abs/1508.06091 (2015) - [i7]Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon:
Extending Gossip Algorithms to Distributed Estimation of U-Statistics. CoRR abs/1511.05464 (2015) - 2014
- [j11]Charanpal Dhanjal, Romaric Gaudel, Stéphan Clémençon:
Efficient eigen-updating for spectral graph clustering. Neurocomputing 131: 440-452 (2014) - [j10]Stéphan Clémençon:
A statistical view of clustering performance through the theory of U-processes. J. Multivar. Anal. 124: 42-56 (2014) - [j9]Stéphan Clémençon, Sylvain Robbiano:
Building confidence regions for the ROC surface. Pattern Recognit. Lett. 46: 67-74 (2014) - [c27]Stéphan Clémençon, Patrice Bertail, Emilie Chautru:
Scaling up M-estimation via sampling designs: The Horvitz-Thompson stochastic gradient descent. IEEE BigData 2014: 25-30 - [c26]Eric Sibony, Stéphan Clémençon, Jérémie Jakubowicz:
Multiresolution analysis of incomplete rankings with applications to prediction. IEEE BigData 2014: 88-95 - [c25]Stéphan Clémençon, Sylvain Robbiano:
Anomaly Ranking as Supervised Bipartite Ranking. ICML 2014: 343-351 - [c24]Charanpal Dhanjal, Stéphan Clémençon:
Learning reputation in an authorship network. SAC 2014: 1724-1726 - [c23]Charanpal Dhanjal, Romaric Gaudel, Stéphan Clémençon:
Online Matrix Completion Through Nuclear Norm Regularisation. SDM 2014: 623-631 - [i6]Stéphan Clémençon, Héctor de Arazoza, Fabrice Rossi, Viet-Chi Tran:
A statistical network analysis of the HIV/AIDS epidemics in Cuba. CoRR abs/1401.6449 (2014) - 2013
- [j8]Stéphan Clémençon, Marine Depecker, Nicolas Vayatis:
Ranking forests. J. Mach. Learn. Res. 14(1): 39-73 (2013) - [j7]Stéphan Clémençon, Sylvain Robbiano, Nicolas Vayatis:
Ranking data with ordinal labels: optimality and pairwise aggregation. Mach. Learn. 91(1): 67-104 (2013) - [j6]Stéphan Clémençon, Marine Depecker, Nicolas Vayatis:
An empirical comparison of learning algorithms for nonparametric scoring: the TreeRank algorithm and other methods. Pattern Anal. Appl. 16(4): 475-496 (2013) - [c22]Stéphan Clémençon, Jérémie Jakubowicz:
Scoring anomalies: a M-estimation formulation. AISTATS 2013: 659-667 - [c21]Pascal Bianchi, Stéphan Clémençon, Gemma Morral, Jérémie Jakubowicz:
On-line learning gossip algorithm in multi-agent systems with local decision rules. IEEE BigData 2013: 6-14 - [c20]Stéphan Clémençon, Sylvain Robbiano, Jessica Tressou:
Maximal Deviations of Incomplete U-statistics with Applications to Empirical Risk Sampling. SDM 2013: 19-27 - [i5]Charanpal Dhanjal, Stéphan Clémençon:
Learning Reputation in an Authorship Network. CoRR abs/1311.6334 (2013) - 2012
- [i4]Stéphan Clémençon, Héctor de Arazoza, Fabrice Rossi, Viet-Chi Tran:
Hierarchical clustering for graph visualization. CoRR abs/1210.5693 (2012) - [i3]Stéphan Clémençon, Héctor de Arazoza, Fabrice Rossi, Viet-Chi Tran:
Visual Mining of Epidemic Networks. CoRR abs/1210.5694 (2012) - [i2]Charanpal Dhanjal, Sandrine Blanchemanche, Stéphan Clémençon, Ákos Róna-Tas, Fabrice Rossi:
Dissemination of Health Information within Social Networks. CoRR abs/1211.4235 (2012) - 2011
- [j5]Stéphan Clémençon, Marine Depecker, Nicolas Vayatis:
Adaptive partitioning schemes for bipartite ranking - How to grow and prune a ranking tree. Mach. Learn. 83(1): 31-69 (2011) - [j4]Stéphan Clémençon, Marine Depecker, Nicolas Vayatis:
Avancées récentes dans le domaine de l'apprentissage d'ordonnancements. Rev. d'Intelligence Artif. 25(3): 345-368 (2011) - [c19]Stéphan Clémençon, Héctor de Arazoza, Fabrice Rossi, Viet-Chi Tran:
Hierarchical clustering for graph visualization. ESANN 2011 - [c18]Sylvain Robbiano, Stéphan Clémençon:
Minimax Learning Rates for Bipartite Ranking and Plug-in Rules. ICML 2011: 441-448 - [c17]Till Wohlfarth, Stéphan Clémençon, François Roueff, Xavier Casellato:
A Data-Mining Approach to Travel Price Forecasting. ICMLA (1) 2011: 84-89 - [c16]Stéphan Clémençon, Héctor de Arazoza, Fabrice Rossi, Viet-Chi Tran:
Visual Mining of Epidemic Networks. IWANN (2) 2011: 276-283 - [c15]Stéphan Clémençon:
On U-processes and clustering performance. NIPS 2011: 37-45 - [c14]Stéphan Clémençon, Romaric Gaudel, Jérémie Jakubowicz:
Clustering Rankings in the Fourier Domain. ECML/PKDD (1) 2011: 343-358 - [c13]Charanpal Dhanjal, Stéphan Clémençon:
Maximising the Quality of Influence. SDM 2011: 956-967 - [i1]Charanpal Dhanjal, Stéphan Clémençon, Héctor de Arazoza, Fabrice Rossi, Viet-Chi Tran:
The Evolution of the Cuban HIV/AIDS Network. CoRR abs/1109.2499 (2011) - 2010
- [c12]Stéphan Clémençon, Jérémie Jakubowicz:
Kantorovich Distances between Rankings with Applications to Rank Aggregation. ECML/PKDD (1) 2010: 248-263
2000 – 2009
- 2009
- [j3]Stéphan Clémençon, Nicolas Vayatis:
Tree-based ranking methods. IEEE Trans. Inf. Theory 55(9): 4316-4336 (2009) - [c11]Stéphan Clémençon, Nicolas Vayatis:
Adaptive Estimation of the Optimal ROC Curve and a Bipartite Ranking Algorithm. ALT 2009: 216-231 - [c10]Stéphan Clémençon, Nicolas Vayatis:
Nonparametric estimation of the precision-recall curve. ICML 2009: 185-192 - [c9]Stéphan Clémençon, Marine Depecker, Nicolas Vayatis:
Bagging Ranking Trees. ICMLA 2009: 658-663 - [c8]Stéphan Clémençon, Nicolas Vayatis, Marine Depecker:
AUC optimization and the two-sample problem. NIPS 2009: 360-368 - [c7]Stéphan Clémençon, Nicolas Vayatis:
On Partitioning Rules for Bipartite Ranking. AISTATS 2009: 97-104 - 2008
- [j2]Patrice Bertail, Stéphan Clémençon:
Approximate regenerative-block bootstrap for Markov chains. Comput. Stat. Data Anal. 52(5): 2739-2756 (2008) - [c6]Stéphan Clémençon, Nicolas Vayatis:
Approximation of the Optimal ROC Curve and a Tree-Based Ranking Algorithm. ALT 2008: 22-37 - [c5]Patrice Bertail, Stéphan Clémençon, Nicolas Vayatis:
On Bootstrapping the ROC Curve. NIPS 2008: 137-144 - [c4]Stéphan Clémençon, Nicolas Vayatis:
Empirical performance maximization for linear rank statistics. NIPS 2008: 305-312 - [c3]Stéphan Clémençon, Nicolas Vayatis:
Overlaying classifiers: a practical approach for optimal ranking. NIPS 2008: 313-320 - 2007
- [j1]Stéphan Clémençon, Nicolas Vayatis:
Ranking the Best Instances. J. Mach. Learn. Res. 8: 2671-2699 (2007) - 2005
- [c2]Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis:
Ranking and Scoring Using Empirical Risk Minimization. COLT 2005: 1-15 - [c1]Stéphan Clémençon, Gábor Lugosi, Nicolas Vayatis:
From Ranking to Classification: A Statistical View. GfKl 2005: 214-221
Coauthor Index
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