Peer-reviewed publications

  • Regular variation in Hilbert spaces and principal component analysis for functional extremes. With N. Huet & A. Sabourin. To appear in Stochastic Processes abd their Applications, 2024.
  • A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions. With G. Staerman, P. Colombo, P. Mozharovskyi & F. d’Alché. To appear in TMLR, 2024.
  • On Ranking-based Tests of Independence. With M. Limnios. To appear in the Proceedings of AISTATS, 2024.
  • Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition. With J. R. Conti. To appear in the Proceedings of ICLR, 2024.
  • Machine Learning-Driven Low-Complexity Optical Power Optimization for Point-to-Point Links Impaired by PDL. With  I. Andrenacci, M. Lonardi, P. Ramantanis, E. Awwad, E. Irurozki & P. Serena. To appear in the Proceedings of OFC, 2024
  • A machine-learning-based technique to establish ASE or Kerr impairment dominance in optical networks. With  I. Andrenacci, M. Lonardi, P. Ramantanis, E. Awwad, E. Irurozki & S. Almonacil. To appear in Journal of Optical Communications and Networking, 2024.
  • Affine-Invariant Integrated Rank-Weighted Statistical Depth: Properties and Finite Sample Analysis. With P. Mozharovskyi & G. Staerman. To appear in Electronic Journal of Statistics, 2023.
  • Active Bipartite Ranking. With J. Cheshire & V. Laurent. To appear in the Proceedings of NeurIPS, 2023.
  • Fighting selection bias in statistical learning: application to visual recognition from biased image databases. With P. Laforgue & R. Vogel. In Journal of Nonparametric Statistics, 2023.
  • A Statistical Learning View of Simple Kriging. With E. Siviero & E. Chautru. To appear in TEST, 2023.
  • Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues. With M. Goibert, E. Irurozki & C. Calauzènes. In the Proceedings of ICML, 2023.
  • Fast and accurate nonlinear interference in-band spectrum prediction for sparse channel allocation. With I. Andrenacci, M. Leonardi, P. Ramantanis, E. Awwad & E. Irurozki. In the Proceedings of ONDM, 2023.
  • Reconstruction of Trajectories of Athletes Using Computer Vision Models and Kinetic Analysis. With Q. Gan, E. Fénaux, O. Jelassi, S.M. Nguyen & M. Yacoubi. In the Proceedings of the MIT SSAC’23 conference, 2023
  • Assessing Performance and Fairness Metrics in Face Recognition – Bootstrap Methods, With J.R Conti. In the NeurIPS workshop on Trustworthy and Socially Responsible Machine Learning (TSRML), 2022. 
  • Universal Aggregation of Permutations. With E. Irurozki. In the Proceedings of DA2PL, 2022.
  • Functional Anomaly Detection: a Benchmark Study. With E. Adjakossa, P. Mozharovskyi & G. Staerman. In the International Journal of Data Science and Analytics, 2022.
  • What are the best Systems? New Perspectives on NLP Benchmarking With P. Colombo, N. Noiry & E. Irurozki. In the Proceedings of NeurIPS, 2022.
  • Statistical Learning from Biased Training Samples. With P. Laforgue. In Electronic Journal of Statistics, 2022
  • Concentration bounds for the empirical angular measure with statistical learning application. With H. Jalalzaï, S. Lhaut, A. Sabourin & J. Segers. In Bernoulli, 2022
  • Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model. With J.R. Conti, N. Noiry, S. Gentric & V. Despeigel. In the Proceedings of ICML, 2022
  • Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications. With M. Goibert (Telecom Paris, Criteo), E. Irurozki (Telecom Paris) & P. Mozharovskyi (Telecom Paris). In the Proceedings of AISTATS, 2022
  • Empirical Risk Minimisation under Random Censorship. With G. Ausset (Telecom Paris) and F. Portier (Ensai). In JMLR, 2022.
  • Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite Ranking. With M. Limnios (ENS Paris Saclay) and N. Vayatis (ENS Paris Saclay). In the Electronic Journal of Statistics, 2021.
  • Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics. With M. Limnios (ENS Paris Saclay) and N. Noiry (Telecom Paris). In the Proceedings of LIDTA, 2021.
  • Individual Survival Curves with Conditional Normalizing Flows. With G. Ausset (Telecom Paris), F. Portier (Telecom Paris). In the Proceedings of IEEE DSAA, 2021.
  • Anomalous Cluster Detection in Large Networks with Diffusion-Percolation Testing. With C. Larroche (Telecom Paris, ANSSI) and J. Mazel (ANSSI). In the Proceedings of ESANN, 2021.
  • Dynamic Graph Convolutional LSTM application for traffic flow estimation from error-prone measurements: results and transferability analysis. With S. Boudabous (Telecom Paris, Vedecom), J. Garbiso (Vedecom) and H. Labiod (Telecom Paris). In the Proceedings of IEEE DSAA, 2021.
  • Learning from Biased Data: A Semi-Parametric Approach. With Y. Guyonvarch (Telecom Paris), N. Noiry (Telecom Paris) and P. Bertail (Université Paris X). In the Proceedings of ICML, 2021.
  • Generalization Bounds in the Presence of Outliers: a Median-of-Means Study. With P. Laforgue (Telecom Paris) and G. Staerman (Telecom Paris). In the Proceedings of ICML, 2021.
  • Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints. With A. Bellet (INRIA) and R. Vogel (Telecom Paris, IDEMIA). In the Proceedings of AISTATS, 2021.
  • Nearest Neighbour Based Estimates of Gradients: Sharp Nonasymptotic Bounds and Applications. With G. Ausset (Telecom Paris, BNP) and F. Portier (Telecom Paris). In the Proceedings of AISTATS, 2021.
  • Dynamically Modelling Heterogeneous Higher-Order Interactions for Malicious Behavior Detection in Event Logs. With C. Larroche (Telecom Paris, ANSSI), J. Mazel (ANSSI). In the Proceedings of CAID, 2020
  • Percolation-Based Detection of Anomalous Subgraphs in Complex Networks. With C. Larroche (Telecom Paris, ANSSI), J. Mazel (ANSSI). In the Proceedings of IDA, 2020.
  • Identifying the “right” level of explanation in a given situation. With V. Beaudoin, I. Bloch D. Bounie, F. D’alché-Buc, J. Eagan, W. Maxwell, P. Mozharovskyi, J. Parekh. In the Proceedings of ECAI, 2020.
  • Weighted Emprirical Risk Minimization: Transfer Learning based on Importance Sampling. With M. Achab (Telecom Paris), C. Tillier (Université Paris 1) and R. Vogel (Telecom Paris, IDEMIA). In the Proceedings of ESANN, 2020.
  • The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure. With P. Mozharovskyi (Telecom Paris) and G. Staermann (Telecom Paris). In the Proceedings of AISTATS, 2020.
  • Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling. With M. Achab (Telecom Paris), C. Tillier (Université Paris 1) and R. Vogel (Telecom Paris, IDEMIA). In the Proceedings of ICMA, 2020.
  • A 1vs1 Multiclass Classification Approach to Label Ranking. With R. Vogel (Telecom Paris). In the Proceedings of AISTATS, 2020.
  • Distributed On-line Anomaly Detection for Connected Vehicles. With H. Chaouchi (Telecom Sud Paris), O. Jelassi (Telecom Paris) and N. Neji (Telecom Paris). In the Proceedings of ICAIIC, 2020.
  • Functional Isolation Forest. With G. Staermann (Telecom Paris), P. Mozharovskyi (Telecom Paris) and F. d’Alché (Telecom Paris). In the Proceedings of ACML, 2019.
  • A Multivariate Extreme Value Theory Approach to Anomaly Clustering and Visualization. With M. Chiapino (Telecom ParisTech), V. Feuillard (Airbus) and A. Sabourin (Telecom ParisTech). In Computational Statistics.
  • Statistical Learning Based On Markovian Data: Maximal Deviation Inequalities and Learning Rates. With G. Ciolek (Telecom ParisTech) and P. Bertail (Université Paris-Ouest). In the Annals of Mathematics and Artificial Intelligence.
  • A LSTM Approach to Detection of Autonomous Vehicle Hijacking. With O. Jelassi (Télécom ParisTech) and N. Negi (Télécom ParisTech). In the Proceedings of VEHITS, 2019.
  • Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning. With R. Vogel (Télécom Paris), A. Bellet (INRIA), O. Jelassi (Télécom Paris) and G. Papa (Télécom Paris). In the Proceedings of ECML, 2019.
  • On Tree-based Methods for Similarity Learning. With R. Vogel (Télécom ParisTech and IDEMIA). In the Proceedings of LOD, 2019.
  • Traffic flow and travel speed estimation through Bluetooth sensing. With S. Boudabous (Vedecom), J. Garbiso (Vedecom), B. Leroy (Vedecom) and H. Labiod (Telecom ParisTech). In the Proceedings of VTC-Spring 2019.
  • On Medians of (Randomized) Pairwise Means. With P. Laforgue (Télécom ParisTech) and P. Bertail (Université Paris X). In the Proceedings of ICML, Longbeach, USA, 2019.
  • Autoencoding any Data through Kernel Autoencoders. With P. Laforgue (Télécom ParisTech) and Florence d’Alché-Buc (Télécom ParisTech). In the Proceedings of AISTATS, Okinawa, Japan, 2019.
  • Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach. With M. Achab (Télécom ParisTech) and A. Korba (UCL). In the Proceedings of ALT, Chicago, USA, 2019.
  • Machine Learning for Survival Analysis: Empirical Risk Minimization for Censored Distribution-Free Regression with Applications. With G. Ausset (Télécom ParisTech) and F. Portier (Télécom ParisTech). In the Proceedings of the ML4H Workshop, NIPS 2018.
  • On Binary Classification in Extreme Regions. With H. Jalalzai (Télécom ParisTech) and A. Sabourin (Télécom ParisTech). In the Proceedings of NEURIPS, Montréal, Canada, 2018.
  • Profitable Bandits. With M. Achab (Télécom ParisTech) and A. Garivier (ENS Lyon). In the Proceedings of ACML, Beijing, China, 2018.
  • Exponential inequalities for conditional Poisson schemes. With P. Bertail (Université Paris Ouest). In Bernoulli, 2018.
  • Optimal survey schemes for stochastic gradient descent with applications to M-estimation. With P. Bertail (Université Paris Ouest), E. Chautru (Mines ParisTech) and G. Papa (Telecom ParisTech). In ESAIM Probability & Statistics, 2018.
  • Mass Volume Curves and Anomaly Ranking. With A. Thomas (Huawei). In Electronic Journal of Statistics, 2018.
  • A secure IoT architecture for streaming data analysis and anomaly detection. With S. Boudabous (Télécom ParisTech), O. Jelassi (Télécom ParisTech) & M. Caros-Roca (Télécom ParisTech). In the Proceedings of IoTSec, 2018, Orlando, Florida, 2018.
  • Transport Mode Detection When Fine-grained and Coarse-grained Data Meet. With F. Asgari (Télécom ParisTech). In the Proceedings of ICITE, 2018, Singapore.
  • A Probabilistic Theory of Supervised Similarity Learning. With R. Vogel (Télécom ParisTech), and A. Bellet (INRIA Lille). In the Proceedings of ICML, 2018, Stockholm, Sweden.
  • On Aggregation in Ranking Median Regression. With A. Korba (Télécom ParisTech). In the Proceedings of ESANN 2018, Bruges, Belgium.
  • Generalization Bounds for Minimum Volume Set Estimation based on Markovian Data. With P. Bertail (Université Paris Ouest) and G. Ciolek (Telecom ParisTech). In the Proceedings of ISAIM 2018, Fort Lauderdale, USA.
  • Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods. With F. Portier (Télécom ParisTech). In the Proceedings of AISTATS 2018, Lanzarote, Spain.
  • Ranking Median Regression: Learning to Order through Local Consensus. With A. Korba (Télécom ParisTech) and E. Sibony. In the Proceedings of ALT 2018, Lanzarote, Spain.
  • Ranking Data with Continuous Labels through Oriented Recursive Partitions. With M. Achab (Télécom ParisTech). In the Proceedings of NIPS 2017, Longbeach, USA.
  • Ranking Median Regression: Learning to Order through Local Consensus. With A. Korba (Télécom ParisTech). In the Proceedings of NIPS 2017, Workshop DISCML, Longbeach, USA.
  • Max-K armed bandit: on the ExtremeHunter algorithm and beyond. With M. Achab (Télécom ParisTech), A. Garivier (Université Paul Sabatier), A. Sabourin (Télécom ParisTech) & C. Vernade (Télécom ParisTech). In the Proceedings of ECML 2017, Skopje, Macedonia.
  • A Learning Theory of Ranking Aggregation. With  A. Korba (Télécom ParisTech) & E. Sibony (Télécom ParisTech). In the Proceedings of AISTATS 2017, Fort Lauderdale, USA.
  • Anomaly Detection in Extreme Regions via Empirical MV-sets on the Sphere. With A. Thomas (Télécom ParisTech), A. Sabourin (Télécom ParisTech) & A. Gramfort (Télécom ParisTech). In the Proceedings of AISTATS 2017, Fort Lauderdale, USA.
  • Sparse Representation of Multivariate Extremes with Applications to Anomaly Detection. With N. Goix (Télécom ParisTech) & A. Sabourin (Télécom ParisTech). In Journal of Multivariate Analysis, 2017.
  • Sampling and Empirical Risk Minimization. With P. Bertail (Université Paris-Ouest) and E. Chautru (Mines ParisTech). In Statistics, 2017.
  • EMOTHAW: A Novel Database for Emotional State Recognition from Handwriting and Drawing. With L. Likforman, A. Espositio, M. Faundes-Zanuy & G. Cordasco. In IEEE Transactions on Human-Machine Systems, November 2016.
  • Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers. With P. Bertail (Université Paris-Ouest) and Guillaume Papa (Telecom ParisTech). In the Proceedings of ACML 2016, Hamilton (New Zealand).
  • Sparse Representation of Multivariate Extremes with Applications to Anomaly Ranking. With N. Goix (Telecom ParisTech) & A. Sabourin (Telecom ParisTech). In the Proceedings of AISTATS 2016, Cadiz (Spain).
  • Empirical processes in survey sampling with (conditional) Poisson designs. With P. Bertail (Université Paris-Ouest) and E. Chautru (Mines ParisTech). In Scandinavian Journal of Statistics, 2016.
  • Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics. With A. Bellet (INRIA) and I. Colin (Télécom ParisTech). In Journal of Machine Learning Research, 2016.
  • Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions. With A. Bellet (INRIA), I. Colin (Telecom ParisTech) and J. Salmon (Telecom ParisTech). In the Proceedings of the 2016 ICML Conference, NYC (USA).
  • Learning Hyperparameters for Unsupervised Anomaly Detection. With A. Thomas (Telecom ParisTech), A. Gramfort (Telecom ParisTech) and V. Feuillard (Airbus). In the Proceedings of the 2016 ICML Anomaly Detection Workshop, NYC (USA), Best Paper Award.
  • On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability. With A. Bellet (INRIA), G. Papa (Telecom ParisTech). In Advances in Neural Information Processing Systems, 2016, Barcelona (Spain).
  • Multiresolution analysis for the statistical analysis of incomplete rankings. With E. Sibony (Télécom ParisTech) & A. Korba (Télécom ParisTech). In NIPS 2015 Workshop on Multiresolution Methods for Large Scale Learning, Montréal Dec. 2015.
  • Sparse Representation of Multivariate Extremes. With N. Goix (Telecom ParisTech) & A. Sabourin (Télécom ParisTech). In NIPS 2015 Workshop on Nonparametric Methods for Large Scale Representation Learning, Montréal Dec. 2015.
  • On Tail Index Estimation based on Multivariate Data. With A. Dematteo (Telecom ParisTech). In Journal of Nonparametric Statistics, (2016), vol. 28, issue 1.
  • On Computer Intensive Simulation and Estimation Methods for Rare Event Analysis in Epidemic Models. With A. Cousien (Lille 1), M. Davilà-Felipe (UPMC), V.C. Tran (Lille 1). In Statistics in Medicine.
  • Extended Gossip Algorithms to Distributed Estimation of U-statistics. With A. Bellet (Telecom ParisTech), I. Colin (Telecom ParisTech) and J. Salmon (Telecom ParisTech). In the Proceedings of the 2015 NIPS Conference, Montréal (Canada).
  • SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk. With A. Bellet (Telecom ParisTech), G. Papa (Telecom ParisTech). In the Proceedings of the 2015 NIPS Conference, Montréal (Canada).
  • A Statistical Network Analysis of the HIV/AIDS Epidemics in Cuba. With H. de Arazoza (Universidad La Habana), M. Rossi (Paris 1), V.C. Tran (Lille 1). In Social Networks Analysis and Mining, 5-58, 2015.
  • MRA-based Statistical Learning from Incomplete Rankings. With J. Jakubowicz (Telecom Sud Paris) & E. Sibony (Telecom ParisTech). In the Proceedings of  the 2015 ICML Conference, Lille (France).
  • Scalability of Stochastic Gradient Descent based on ”Smart” Sampling Techniques. With A. Bellet (Telecom ParisTech), O. Jelassi (Telecom ParisTech) & G. Papa (Telecom ParisTech). In the Proceedings of the 2015 INNS Big Data Conference, San Francisco (USA).
  • Adaptive Sampling for Incremental Optimization using Stochastic Gradient Descent. With P. Bianchi (Telecom ParisTech) & G. Papa (Telecom ParisTech). In the Proceedings of the 2015 Conference on Algorithmic Learning Theory, Banff (Canada).
  • Learning the dependence structure of rare events: a non asymptotic study. With N. Goix (Telecom ParisTech) & A. Sabourin (Telecom ParisTech). In the Proceedings of the 2015 COLT Conference, Paris (France).
  • An Ensemble Learning Technique for Multipartite Ranking. With S. Robbiano (UCL), 2015. In the proceedings of the ESANN 2015 special session «Beyond classification: confidence, rejection, AUC optimization, etc.», Bruges (Belgium).
  • On Anomaly Ranking and Excess-Mass Curves. With N. Goix (Telecom ParisTech) & A. Sabourin (Telecom ParisTech). In the Proceedings of the AISTATS 2015, San Diego (California, USA).
  • Extreme Value Statistics for Markov Chains with Applications to Finance and Insurance. With P. Bertail (Université Paris-Ouest) & C. Tillié (Université Paris-Ouest). In the Proceedings of the Conference on  Extreme Events in Finance, Royaumont (Asnière sur Oise, France).
  • Collaborative Filtering with Localised Ranking. With R. Gaudel (Lille 1) and C. Dhanjal (Télécom ParisTech) (2015). In Proceedings of AAAI‘2015, Austin (Texas, USA).
  • Multiresolution analysis of incomplete rankings with applications to prediction. With Jérémie Jakubowicz (Telecom Sud Paris) & E. Sibony (Telecom ParisTech). In the Proceedings of  the 2014 IEEE Big Data – Workshop on Scalable Machine Learning, Washington (USA).
  • Bootstrapping Robust Statistics for Markovian Data – Applications to Regenerative  R- and L- statistics. With P. Bertail (Université Paris-Ouest) & J. Tressou (INRA). In Journal of Time Series Analysis, Volume 36, Issue 3, pages 462–480, 2015.
  • The TreeRank Tournament Algorithm for Multipartite Ranking. With S. Robbiano (University College of London). In Journal of Nonparametric Statistics, Volume 27, Issue 1, pages 107-126.
  • Scaling-up M-estimation via sampling designs: the Horvitz-Thompson stochastic gradient descent. With P. Bertail (Université Paris-Ouest) & E. Chautru (Mines ParisTech). In the Proceedings of  the 2014 IEEE International Conference on Big Data, Washington (USA).
  • Matrix Completion with Nuclear Norm Regularisation in a Sequential Context. With R. Gaudel (Lille 1) and C. Dhanjal (Télécom ParisTech) (2014). In Proceedings of CAp‘2014, Saint-Etienne (France).
  • Learning the Graph of Relations Among Multiple Tasks. With R. Argyriou (Centrale Paris) and R. Zhang (Télécom ParisTech) (2014). In Proceedings of CAp‘2014, Saint-Etienne (France).
  • Improving dietary exposure models by imputing biomonitoring data through ABC methods. With C. Béchaux (ANSES) and A. Crépet (ANSES). In International Journal of Biostatistics, Volume 6, Issue 2,  pages 277-287, 2014.
  • PBPK and population modelling to interpret urine cadmium concentrations of the French population. With C. Béchaux (ANSES), L. Bodin (ANSES) and A. Crépet (ANSES). In Toxicology and Applied Pharmacology, pages 364-372, 2014.
  • An Empirical Comparison of V-fold Penalisation and Cross Validation for Model Selection in Distribution-Free Regression. With C. Dhanjal (Télécom ParisTech), Nicolas Bakiotis (UPMC) and Nicolas Usunier (UTC). In Pattern Analysis and its Applications, Volume 19, Number 1, pages 41-53, 2016.
  • Tail Index Estimation Based on Survey Data. With P. Bertail (Université Paris-Ouest) and E. Chautru (Télécom ParisTech). In ESAIM Probability & Statistics, Volume 19, pages 18-59, 2015.
  • Anomaly Ranking as Supervised Bipartite Ranking. With S. Robbiano (Télécom ParisTech). In the Proceedings of the International Conference in Machine Learning ICML’14, Beijing (China), 2014.
  • Learning the Graph of Relations Among Multiple Tasks. With A. Argyriou (Centrale Paris) and R. Zhang. In the Proceedings of ICML 2014, Workshop on New Learning Models and Frameworks for Big Data, 2014.
  • Building confidence regions for the ROC surface. With S. Robbiano (Télécom ParisTech). In Pattern Recognition Letters, Volume 46, pages 67-74, (2014).
  • Efficient Eigen-updating for Spectral Graph Clustering. (2014). With C. Dhanjal (Télécom ParisTech) and R. Gaudel (Lille 3). In Neurocomputing. Volume 131, pages 440-452, (2014).
  • Learning Reputation in an Authorship Network. With C. Dhanjal (Télécom ParisTech). In the Proceedings of the 29-th ACM Symposium on Applied Computing, Gyeongju (Corée), Mars 2014.
  • Online Matrix Completion Through Nuclear Norm Regularisation. With C. Dhanjal (Télécom ParisTech) and R. Gaudel (Lille 3). In the Proceedings of the SIAM International Conference on Data-Mining SDM14, Philadelphia (USA), 2014.
  • A stochastic graph-based model for epidemics. With C. Dhanjal (Télécom ParisTech). In the Proceedings of the SIAM International Conference on Data-Mining SDM14-Networks, Philadelphia (USA), 2014.
  • On Recent Advances in Supervised Ranking for Metabolite Profiling. With C. Dhanjal (Télécom ParisTech). In the Proceedings of the SIAM International Conference on Data-Mining SDM-DMMH, Philadelphia (USA), 2014.
  • A statistical view of clustering performance through the theory of U-processes. (2014), In Journal of Multivariate Analysis, Vol. 124, pp 42-56.
  • On-Line Learning Gossip Algorithm in Multi-Agent Systems with Local Decision Rules. With P. Bianchi (Telecom ParisTech), J. Jakubowicz (Telecom Sud Management) & G. Morral Adel (Telecom ParisTech). In the Proceedings of  the 2013 IEEE International Conference on Big Data, Santa Clara (USA).
  • Scoring anomalies : a M-estimation formulation. With J. Jakubowicz (Telecom Sud-Management) (2013). In JMLR W&CP, Vol. 31, Proceedings of AISTATS 2013, Scottsdale (USA).
  • Regenerative Block-Bootstrap Confidence Intervals for the Tail and Extremal Indexes. With P. Bertail (Paris X) and J. Tressou (INRA) (2013). In Electronic Journal of Statistics. Vol. 7, pp 1224-1248.
  • Maximal Deviations of Incomplete U-statistics with Applications to Empirical Risk Sampling. With S. Robbiano (Telecom ParisTech) and J. Tressou (INRA) (2013). In the Proceedings of the SIAM International Conference on Data-Mining SDM13, Austin (USA).
  • Ranking Data with Ordinal Labels: Optimality and Pairwise Aggregation. With S. Robbiano (Telecom ParisTech) and N. Vayatis (ENSC) (2013). In Machine Learning. Vol. 93, No. 1, pp 67-104.
  • Ranking Forests. With M. Depecker (Telecom ParisTech) and N. Vayatis (ENSC) (2013). In  Journal of Machine Learning Research, Vol. 14, pp 39-73.
  • An Empirical Comparison of Learning Algorithms for Nonparametric Scoring – The TreeRank Algorithm and Other Methods. With M. Depecker (Telecom ParisTech) and N. Vayatis (ENSC) (2013). In  Pattern Analysis and its Applications, Vol. 16, No. 4, pp 475-496.
  • Deterministic modeling for transmission of Human Papillomavirus 6/11: impact of vaccination. With L. Majed (INSERM – Paris V) and R. Lounes (Paris V). In Mathematical Medicine and Biology, (2013).
  • Impact of human papillomavirus vaccination on anal cancer incidence in French women. (2012). With L. Majed (INSERM – Paris V) and R. Lounes (Paris V). In Journal of Public Health and Epidemiology, vol. 4, No. 5, pp 141-149.
  • Efficacy of Vaccination against HPV Infections to Prevent Cervical Cancer in France: Present Assessment and Pathways to Improve Vaccination Policies. With L. Majed (INSERM – Paris V) and  R. Lounes (Paris V) (2012). In Plos One, vol. 7, n°3.
  • New approach for the assessment of cluster diets. (2013) With T. Barré (WHO), M. Feinberg (INRA), P. Verger (WHO), A. Crépet (ANSES) and M. Sy (INRA). In Food and Chemical Toxicology, Vol. 52, pp 180-187.
  • Calibrating SVMs with V-fold penalization. (2011). With N. Baskiotis (UPMC), N. Usunier (UPMC). In Proceedings of NIPS 2011, Workshop on Model Order Selection, Granada (Spain).
  • Incremental Spectral Clustering with the Normalised Laplacian. (2011). With C. Dhanjal (UPMC) and R. Gaudel (Lille 3). In Proceedings of NIPS 2011, Workshop on Discrete Optimization in Machine-Learning, Granada (Spain).
  • A data-mining approach to travel price forecasting. With T. Wohlfarth (Telecom ParisTech), F. Roueff (Telecom ParisTech) and X. Casellato (Liligo). In Proceedings of the IEEE International Conference in Machine Learning and its Applications 2012, Hawaï (USA).
  • On U-processes and clustering performance. (2011). In Advances in Neural Information Processing Systems 2011, Granada (Spain).
  • Nonparametric scoring methods as a support decision tool for medical diagnosis. The TreeRank algorithm and its variants. (2011). With M. Depecker (CEA-LIST) and N. Vayatis (ENSC). In Proceedings of ECML PKDD 2011, Workshop on Knowledge Discovery in Health Care and Medicine, Athens (Greece).
  • On Clustering Rank Data in the Fourier Domain. With R. Gaudel (Telecom ParisTech) and J. Jakubowicz (Telecom ParisTech). In Proceedings of the European Conference in Machine Learning 2011, Athens (Greece).
  • Prédiction de l’occurence d’une baisse de prix pour le conseil à l’achat d’un billet en ligne. With T. Wohlfarth (Telecom ParisTech), F. Roueff (Telecom ParisTech) and X. Casellato (Liligo). In Proceedings of GRETSI 2011.
  • Minimax Learning Rates for Bipartite Ranking and Plug-in rules. With S. Robbiano (Telecom ParisTech) (2011). In Proceedings of the International Conference in Machine Learning 2011, Seattle (USA).
  • Le Filtrage collaboratif vu comme un problème de consensus d’ordonnancements. With R. Gaudel (Telecom ParisTech) (2011). In Proceedings of CAp‘2011, Chambéry (France).
  • A wavelet-based approach to functional bipartite ranking. With M. Depecker (CEA, LIST) (2011). In Proceedings of IEEE Statistical Signal Processing 2011, Nice (France). Long version available at: https://arxiv.org/abs/1312.5066.
  • On maximizing influence. With C. Dhanjal (Telecom ParisTech). In Proceedings of The SIAM Conference on Data Mining  2011, Phoenix (USA).
  • Hierarchical Clustering for Graph Visualization. With H. de Arazoza, V.C. Tran and F. Rossi (2011). In Proceedings of the European Symposium on Artifical Neural Networks, ESANN 2011 Bruges (Belgium).
  • Visual Mining of Epidemic Networks. With H. de Arazoza, V.C. Tran and F. Rossi (2011). In Proceedings of the International Work Conference on Artifical Neural Networks, IWANN 2011 Torremolinos (Spain).
  • A Renewal Approach to Markovian U-statistics. With P. Bertail (Paris X) (2011). In Mathematical Methods of Statistics, Vol. 20,  N° 2, 79-105.
  • Extraction of food consumption systems by non-negative matrix factorization (NMF) for the assessment of food choices. With M. Zetlaoui (INRA), P. Verger (WHO) and M. Feinberg (INRA) (2011). In Biometrics, Vol. 67, Issue 4 Pages 1185–1679.
  • Adaptive partitioning schemes for bipartite ranking: how to grow and prune a ranking tree. With M. Depecker (Telecom ParisTech) and N. Vayatis (ENSC) (2010). In Machine Learning, Vol. 43, N° 1, 31-69.
  • Données avec label binaire: avancées récentes dans le domaine de l’apprentissage statistique d’ordonnancements. With M. Depecker (Telecom ParisTech) and N. Vayatis (ENSC) (2011). In Revue d’Intelligence Artificielle, Vol. 25, N°3, 345-368.
  • Statistical analysis of a dynamic model for food contaminant exposure with applications to dietary methylmercury contamination. With P. Bertail (Paris X) & J. Tressou (INRA), (2010). In Journal of Biological Dynamics, Volume 4, Issue 2, 212 – 234.
  • Tree-based ranking methods. With N. Vayatis (ENSC) (2009). In IEEE Transactions on Information Theory, Vol. 55, N° 9, 4316-433.
  • The RankOver algorithm: overlaid classification rules for optimal ranking. with N. Vayatis (ENSC) (2010). In Constructive Approximation, Vol. 32, 619-648.
  • Exposition aux risques alimentaires et processus stochastiques, (2009). With J. Tressou (INRA). Journal de la Société Française de Statistique, Vol. 150, N° 1, 3-29.
  • Extreme values statistics for Harris Markov chains via the (pseudo-) regenerative method, (2009). With P. Bertail (CREST Paris X) & J. Tressou (INRA) in Extremes, vol. 12, No. 4, 327-360.
  • Sharp Bounds for the Tails of Functionals of Harris Markov Chains (2010). With P. Bertail (CREST Paris X) in Theory of Probability and its Applications, Vol. 54(3), 505-515.
  • Kantorovich distances between rankings with applications to rank aggregation. With J. Jakubowicz (Telecom ParisTech). In the Proceedings of ECML 2010, European Conference in Machine-Learning, Barcelona (Spain).
  • Services Objectivization : a Ranking Approach. With M. Depecker (Telecom ParisTech) and A. Saint-Marcoux (Renault – Dream DTAA). In the IEEE proceedings of SEDM 2010, International Conference on Software Engineering and Data Mining, Chengdu (China).
  • Données avec label binaire: avancées récentes dans le domaine de l’apprentissage statistique d’ordonnancements. With M. Depecker (Telecom ParisTech) and N. Vayatis (ENSC) (2010). In the Proceedings of CAp‘2010, Clermont-Ferrand (France).
  • Bagging Ranking Trees. With M. Depecker (Telecom ParisTech) and N. Vayatis (ENSC) (2009). In  Proceedings of the IEEE International Conference in Machine Learning and its Applications 2009 Miami (USA), 658-663.
  • AUC maximization and the two-sample problem. With M. Depecker (Telecom ParisTech) and N. Vayatis (ENSC) (2009). In Advances in Neural Information Processing Systems 2010, Vancouver (Canada). 
  • Adaptive estimation of the optimal ROC curve and a bipartite ranking algorithm. With N. Vayatis (ENSC) (2009). In Proceedings of ALT’09, Porto (Portugal) 3-5 Oct. 2009. Algorithmic Learning Theory, 216-231, Lecture Notes in Science, Vol. 5801 Springer, Berlin/Heidelberg.
  • Approximate Regenerative Block-Bootstrap for Hidden Markov Models, with A. Garivier (CNRS) & J. Tressou (Hong Kong UST). In Proceedings of IEEE Statistical Signal Processing, SSP‘2009, Cardiff, Whales (UK).
  • Nonparametric Estimation of the Precision-Recall Curve. With N. Vayatis (ENSC) (2009). In Proceedings of ICML’09, International Conference in Machine Learning, 185-192 Montréal, Canada.    
  • On Partitioning Rules for Bipartite Ranking. With N. Vayatis (ENSC) (2009), Proceedings of The Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR: W&CP 5, 97-104, Clearwater Beach, Florida, April 16-18, 2009.
  • On Bootstrapping the ROC curve, (2009). With N. Vayatis (ENSC) & P. Bertail (Paris X), in Advances in Neural Information Processing Systems 21, eds D. Koller and D. Schuurmans and Y. Bengio and L. Bottou, 137-144.
  • Overlaying classifiers: a practical approach for optimal ranking, (2009). With N. Vayatis (ENSC), in Advances in Neural Information Processing Systems 21, eds D. Koller and D. Schuurmans and Y. Bengio and L. Bottou, 313-320.                                          
  • Empirical performance maximization based on linear rank statistics, (2009). With N. Vayatis (ENSC), in Advances in Neural Information Processing Systems 21, eds D. Koller and D. Schuurmans and Y. Bengio and L. Bottou, 305-312.
  • Approximation of the optimal ROC curve and a tree-based ranking algorithm (2008) with N. Vayatis (ENSC). In Proceedings of ALT’08, Budapest (Hungary) 13-18 Oct. 2008. Algorithmic Learning Theory, 22-37, Lecture Notes in Science, Vol. 5254, eds Y. Freund, L. Györfi, G. Turan, T. Zeugmann, Springer.
  • A Stochastic Epidemic Model with Contact-Tracing: Large Population Approximation and Statistical Estimation (2008). With H. de Arazoza (Universidad La Habana, Cuba)  & V.C. Tran (Lille 1), in Journal of Biological Dynamics, Vol. 2, N° 4, 392-414.
  • A storage model with random release rate for modeling exposure to food contaminant. With P. Bertail (CREST) & J. Tressou (INRA), (2008), in Mathematical Biosciences and Engineering, Vol. 5, N° 1, 35-60.  
  • The AIDS epidemy in Cuba: why a low prevalence? (2007) With H. de Arazoza (Universidad La Habana, Cuba) & B. Auvert (INSERM, Paris V), in BMC Infectious Disease, 7:130.   
  • A regeneration-based runs estimator for the extremal index in the Markov setup (2008), with P. Bertail (CREST Paris X) & J. Tressou (INRA), in Proceedings of IWAP’08, International Workshop in Applied Probability, UTC, France.
  • Regenerative Block-Bootstrap Confidence Intervals for the Extremal Index, (2008) with P. Bertail (CREST Paris X) & J. Tressou (INRA), in Proceedings of IWAP’08, International Workshop in Applied Probability, UTC, France.   
  • New concepts for in-house method validation based on bootstrap tolerance intervals (2007). With M. Feinberg (INRA) & T. Rebafka (CEA/Telecom Paristech) in Chemometrics and Intelligent Laboratory Systems, Vol. 89, N° 2, 69-81.
  • Integration of time as a description parameter in risk characterisation: application to methyl mercury. (2007) with Ph. Verger (INRA) & J. Tressou (INRA), in Regulatory, Toxicology and Pharmacology, Vol. 49, No. 1, 25-30.
  • On Ranking the Best Instances (2007). With N. Vayatis (ENS Cachan), in Journal of Machine Learning Research, Vol. 8, 2671-2699.  
  • Some comments on ”Local Rademacher complexities and oracle inequalities in risk minimization” by Vladimir Koltchinskii. With G. Lugosi (Pompeu Fabra, Barcelona) &  N. Vayatis (Paris VI), (2006)  in Annals of Statistics, Vol. 34, N° 6, 2672-2676. 
  • On Portfolio Selection under Extreme Risk Measure : the Heavy-tailed ICA Model, (2007). With S. Slim (THEMA, Paris X),  International Journal of Theoretical and Applied Finance, Vol. 10, N° 3, May 2007 issue, 449-474.
  • Ranking and Empirical Minimization of U-statistics. With G. Lugosi (Pompeu Fabra, Barcelone) & N. Vayatis (Paris VI), (2008), in Annals of Statistics, Vol. 36, N° 2, 844-874.  
  • Approximate regenerative block-bootstrap : some simulation studies.  With P. Bertail (CREST), (2008), in Computational Statistics and Data Analysis, Vol. 52, N° 5, 2739-2756. 
  • Regenerative block-bootstrap for Harris Markov chains. With P. Bertail (CREST), (2006), Bernoulli, 12(4), 689-712.   
  • Regeneration-based statistics for Harris Markov chains. With P. Bertail (CREST Paris X), (2006), in ‘Dependence in Probability and Statistics’, Eds P. Bertail, P. Doukhan & P. Soulier, Lecture Notes in Statistics N° 187,  Springer-Verlag, 1-54.
  • Edgeworth expansions for suitably renormalized sample mean statistics of atomic Markov chains. With P. Bertail (CREST), (2004), Probability Theory and Related Fields, N° 130,   388-414. 
  • Note on the regeneration-based bootstrap for atomic Markov chains. With P. Bertail (CREST Paris X), (2007), in TEST, Vol. 16, 109–122. 
  • From Classification to Ranking: a Statistical View. With G. Lugosi (Pompeu Fabra, Barcelona) & N. Vayatis (Paris VI), (2006), in Proceedings of the 29th Annual Conference of the German Classification Society, GfKl 2005, ‘Studies in Classification, Data Analysis and Knowledge Organization’ series, Vol. 30, 214-221, Springer-Verlag. 
  • Ranking and scoring, using empirical risk minimization. With G. Lugosi (Université Pompeu Fabra, Barcelone) &  N. Vayatis (Paris VI), (2005) in Proceedings of Computational Learning Theory, COLT’05  Bertinoro, Italy, June 27-30, 2005. Lecture Notes in Computer Science 3559 Springer, 1-15.
  • Second order validity of the 2-split ARBB for Markov chains.  With P. Bertail (CREST), (2004), in COMPSTAT 2004, Proceedings in Computational Statistics, 16th Symposium Held in Prague, Czech Republic Physica-Verlag, 679-688.         
  • Statistical analysis of financial time series under the assumption of local stationarity. With S. Slim (Paris X), (2004), Quantitative Finance, Vol. 4, N° 2, 208-220.
  • Adaptive Estimation of the Transition Density of a Regular Markov Chain by Wavelet Methods. (2000), Mathematical Methods of Statistics, Vol. 9, N° 4, 323-357
  • Moment and Probability Inequalities for Sums of Bounded Additive Functionals of a Regular Markov Chains via the Nummelin Splitting Technique. (2001), Statistics and Probability Letters, 55, 227-238.                                              

Book Chapters

  • Dissemination of Health Information within Social Networks. With S. Blanchemanche (INRA), C. Dhanjal, (Telecom ParisTech), A. Rona-Tas (UC San Diego) and Fabrice Rossi (Telecom ParisTech). In Networks in Social Policy Problems, Cambridge University Press. Edited by Marco Scotti & Balazs Vedres. (2012)
  • Modèles de Ruine – Applications en Santé. Avec P. Bertail (Paris X) et J. Tressou (INRA) (2010). Dans  Approches Statistiques du Risque, édité par la Société Française de Statistique.
  • Anomaly Ranking in a High Dimensional Space: the Unsupervised TreeRank Algorithm. With N. Baskiotis (UPMC) & N. Vayatis (ENS Cachan). In Unsupervised Learning Algorithms, pages 33-54, edited by  Celebi, M. Emre & Aydin, Kemal, Springer (2016).
  • Extreme Values Statistics for Markov Chains with Applications to Finance and Insurance. With P. Bertail (Université Paris-Ouest) & C. Tillé (Université Paris-Ouest). In Handbook on Extreme Events in Finance – Extreme Value Theory and Its Applications, Wiley. Edited by F. Longin.

Patents

  • Patent US 9235805 B2: Method for providing with a score an object, and decision-support system.
  • Patent WO 2012032118 A2 Méthode d’octroi d’un score à un objet, et système d’aide à la décision

Theses & Reports

  • Wavelet methods for nonparametric statistics based on Markovian data (2000) PhD Thesis in Applied Mathematics Université Denis Diderot Paris VII.
  • Note on the practical implementation of two algorithms for estimating the transition density of a regular Markov chain (2002) Technical Report of University Paris X.
  • Nonparametric Estimation for Some Specific Classes of Hidden Markov Models (2003). Technical Report of University Paris X N° 03-9.
  • Habilitation Thesis (2006). University Paris X.

Miscellaneous

  • Les mégadonnées et l’essor de l’intelligence artificielle. Cahiers Français, 2021.
  • Why facial recognition algorithms can’t be perfectly fair? With W. Maxwell. In the Conversation, 2020.
  • Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach. Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, James Eagan, et al.. 2020
  • Algorithmes : biais, discrimination et équité. Avec P. Bertail, D. Bounie and P. Waelbroeck. In Algorithmes et discrimination : les biais dans les RH, Dossier HRM n°58 (2020), HR Today, Ed. ALMA Medien AG
  • Big Data: enjeux technologiques et impact scientifique. Annales des Mines – Enjeux Numériques. Juin 2018.
  • Big Data: Le défi de la formation. TEC – Mobilité intelligente, No. 237. Avril 2018.
  • Intelligence Artificielle: défis scientifiques et attentes socio-économiques. Annales de Mines – Enjeux Numériques. Mars 2018.
  • Le Mastère Spécialisé Big Data de Télécom ParisTech. Journal de la Société Française de Statistique, 2015.
  • Big Data: les enjeux de la formation. Génie Logiciel. Septembre 2014.
  • Les métiers du Big Data. Revue Documentaliste – Sciences de l’Information. Décembre 2014.