174 publications et pour en savoir plus, la liste de publications par axe de recherche :

 

Analyse prévisionnelle des séries temporelles et des flux de données (axe de recherche n°1)

  1. Ekhine Irurozki, Jesus Lobo, Aritz Perez, Javier Del Ser. “Rank aggregation for non-stationary data streams”. European conference on Machine Learning, ECML 2021.
  2. Michael Fell, Yaroslav Nechaev, Gabriel Meseguer-Brocal, Elena Cabrio, Fabien Gandon, Geoffroy Peeter. “Lyrics segmentation via bimodal text–audio representation”. Natural Language Engineering, 1-20. 2021.
  3. Dimitri Bouche, Marianne Clausel, François Roueff, Florence d’Alché-Buc. “Nonlinear Functional Output Regression: A Dictionary Approach”. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 130:235-243, 2021.
  4. Tepmony Sim, Randal Douc, François Roueff. “General-order observation-driven models: Ergodicity and consistency of the maximum likelihood estimator”. Electronic Journal of Statistics, 15(1): 3349-3393. 2021.
  5. Randal Douc, François Roueff, Tepmony Sim. “Necessary and sufficient conditions for the identifiability of observation-driven models”.  Journal of Time Series Analysis, 42: 140-160. 2021.
  6. Amaury Durand, François Roueff, Jean-Marc Jicquel, Nicolas Paul. “Smooth nonnegative tensor factorization for multi-sites electrical load monitoring”. The 29th European Signal Processing Conference, EUSIPCO 2021.
  7. Guillaume Ausset, Tom Ciffreo, Francois Portier, Stephan Clémençon, Timothée Papin. “Individual Survival Curves with Conditional Normalizing Flows”. To appear Proceedings of IEEE DSAA, 2021.
  8. Ben Halstead, Yun Sing Koh, Patricia Riddle, Russel Pears, Mykola Pechenizkiy, Albert Bifet. “Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency”. Data Min. Knowl. Discov. 35(3): 796-836. 2021.
  9. José del Campo-Ávila, Abdelatif Takilalte, Albert Bifet, Llanos Mora López. “Binding data mining and expert knowledge for one-day-ahead prediction of hourly global solar radiation”. Expert Syst. Appl. 167: 114147. 2021.
  10. Natalia Mordvanyuk, Beatriz López, Albert Bifet. “vertTIRP: Robust and efficient vertical frequent time interval-related pattern mining”. Expert Syst. Appl. 168: 114276. 2021.
  11. Eva García-Martín, Albert Bifet, Niklas Lavesson. “Energy modeling of Hoeffding tree ensembles”. Intell. Data Anal. 25(1): 81-104. 2021.
  12. Heitor Murilo Gomes, Jesse Read, Albert Bifet, Robert J. Durrant. “Learning from evolving data streams through ensembles of random patches”. Knowl. Inf. Syst. 63(7): 1597-1625. 2021
  13. Maroua Bahri, Albert Bifet, João Gama, Heitor Murilo Gomes, Silviu Maniu. “Data stream analysis: Foundations, major tasks and tools”. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 11(3). 2021.
  14. Nicolas Kourtellis, Herodotos Herodotou, Maciej Grzenda, Piotr Wawrzyniak, Albert Bifet. “S2CE: a hybrid cloud and edge orchestrator for mining exascale distributed streams”. DEBS 2021: 103-113
  15. Ben Halstead, Yun Sing Koh, Patricia Riddle, Mykola Pechenizkiy, Albert Bifet, Russel Pears. “Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information”. ICDE 2021: 1056-1067.
  16. Wenbin Zhang, Albert Bifet, Xiangliang Zhang, Jeremy C. Weiss, Wolfgang Nejdl. “FARF: A Fair and Adaptive Random Forests Classifier”. PAKDD (2) 2021: 245-256. 2021.
  17. Julian Neri, Philippe Depalle, Roland Badeau. “Approximate Inference and Learning of State Space Models with Laplace Noise”. IEEE Transactions on Signal Processing, 2021.
  18. Kilian Schulze-Forster, Clement S J Doire, Gaël Richard, and Roland Badeau. “Phoneme Level Lyrics Alignment and Text-Informed Singing Voice Separation”. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29:  2382 – 2395. 2021.
  19. Julian Neri, Roland Badeau, and Philippe Depalle. Unsupervised Blind Source Separation with Variational Auto-Encoders. In 29th European Signal Processing Conference, EUSIPCO 2021.
  20. Julian Neri, Philippe Depalle, Roland Badeau. “Damped Chirp Mixture Estimation via Nonlinear Bayesian Regression”. In 23rd International Conference on Digital Audio Effects (DAFx2020), 2021.
  21. Guillaume Doras, Furkan Yesiler, Joan Serra, Emilia Gomez, Geoffroy Peeters. “Combining musical features for cover detection”. Proceedings of the 21st International Society for Music Information Retrieval Conference. ISMIR, 279-286. 2020.
  22. Hadrien Foroughmand, Geoffroy Peeters. “Extending deep rhythm for tempo and genre estimation using complex convolutions, multitask learning and multi-input network”. The 2020 Joint Conference on AI Music Creativity (Computer Simulation of Musical Creativity & The International Workshop on Musical Metacreatio). 2020.
  23. Sophie Achard, Marianne Clause, Irène Gannaz, François Roueff. “New results on approximate Hilbert pairs of wavelet filters with common factors”. Applied and Computational Harmonic Analysis, 49 (3): 1025-1045. 2020.
  24. Paul Doukhan, François Roueff, Joseph Rynkiewicz. « Spectral estimation for non-linear long range dependent discrete time trawl processes ». Electronic Journal of Statistics, 14 (2), 3157-3191. 2020.
  25. Naman Negi, Ons Jelassi, Hakima Chaouchi, Stéphan Clémençon. “Distributed online Data Anomaly Detection for connected vehicles”. 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020.
  26. Corentin Larroche, Johan Mazel, Stéphan Clémençon. “Percolation-Based Detection of Anomalous Subgraphs in Complex Networks”. Symposium on Intelligent Data Analysis, IDA 2020.
  27. Corentin Larroche, Johan Mazel, Stephan Clémençon. “Dynamically Modelling Heterogeneous Higher-Order Interactions for Malicious Behavior Detection in Event Logs”. In the Proceedings of CAID 2020.
  28. Julian Neri, Philippe Depalle, Roland Badeau. “Laplace state space filter with exact inference and moment matching”. In 45th International Conference on Acoustics, Speech, and Signal Processing, 2020.
  29. Julian Neri, Roland Badeau, Philippe Depalle. “Probabilistic filter and smoother for variational inference of Bayesian linear dynamical systems”. In 45th International Conference on Acoustics, Speech, and Signal Processing, 2020.
  30. Naman Negi, Ons Jelassi, Hakima Chaouchi, Stéphan Clémençon. “Distributed online Data Anomaly Detection for connected vehicles“. ICAIIC 2020: 2nd International Conference on Artificial Intelligence in Information and Communication.
  31. Pierre Lafaye de Micheaux, Pavlo Mozharovskyi, Myriam Vimond. “Depth for Curve Data and Applications“. Journal of the American Statistical Association: pages 1-17, 2020.
  32. Achille Aknin, Théophile Dupré, Roland Badeau. “Evaluation of a stochastic reverberation model based on the image source principle”. In International Conference on Digital Audio Effects, 2020.
  33. Aidan Meacham, Roland Badeau, Jean-Dominique Polack. “Auralization of a Hybrid Sound Field using a Wave-Stress Tensor Based Model”. In Forum Acusticum, pages 523–529, 2020.
  34. Maciej Grzenda, Heitor Murilo Gomes, Albert Bifet. “Delayed labelling evaluation for data streams”. Data Min. Knowl. Discov. 34(5): 1237-1266. 2020.
  35. Nedeljko Radulovic, Dihia Boulegane, Albert Bifet. “SCALAR – A Platform for Real-time Machine Learning Competitions on Data Streams”. J. Open Source Softw. 5(55): 2676. 2020.
  36. Yiyan Qi, Jiefeng Cheng, Xiaojun Chen, Reynold Cheng, Albert Bifet, Pinghui Wang. “Discriminative Streaming Network Embedding”. Knowl. Based Syst. 190: 105138. 2020.
  37. Jesus L. Lobo, Izaskun Oregi, Albert Bifet, Javier Del Ser. “Exploiting the stimuli encoding scheme of evolving Spiking Neural Networks for stream learning”. Neural Networks 123: 118-133. 2020.
  38. Wenbin Zhang, Albert Bifet. “FEAT: A Fairness-Enhancing and Concept-Adapting Decision Tree Classifier”. DS 2020: 175-189.
  39. Vítor Cerqueira, Heitor Murilo Gomes, Albert Bifet. “Unsupervised Concept Drift Detection Using a Student-Teacher Approach”. DS 2020: 190-204.
  40. Maroua Bahri, Albert Bifet, Silviu Maniu, Rodrigo Fernandes de Mello, Nikolaos Tziortziotis. “Compressed k-Nearest Neighbors Ensembles for Evolving Data Streams”.ECAI 2020: 961-968.
  41. Maroua Bahri, Bernhard Pfahringer, Albert Bifet, Silviu Maniu. “Efficient Batch-Incremental Classification Using UMAP for Evolving”. Data Streams. IDA 2020: 40-53.
  42. Maroua Bahri, Albert Bifet, Silviu Maniu, Heitor Murilo Gomes. “Survey on Feature Transformation Techniques for Data Streams”. IJCAI 2020: 4796-4802.
  43. Matthias Carnein, Heike Trautmann, Albert Bifet, Bernhard Pfahringer. “confStream: Automated Algorithm Selection and Configuration of Stream”. Clustering Algorithms. LION 2020: 80-95.
  44. Jesus L. Lobo, Javier Del Ser, Albert Bifet, Nikola K. Kasabov. “Spiking Neural Networks and online learning: An overview and perspectives”. Neural Networks 121: 88-100. 2020.
  45. Pierre Laforgue, Alex Lambert, Luc Brogat-Motte, Florence d’Alché-Buc. “Duality in RKHSs with Infinite Dimensional Outputs: Application to Robust Losses”. 37th International Conference on Machine Learning (ICML), 2020, PMLR 119, pages 5598-5607.
  46. Kilian Schulze-Forster, Clément Doire, Gael Richard, Roland Badeau. “Joint phoneme alignment and text-informed speech separation on highly corrupted speech”. 45th International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020.
  47. Randal Douc, Jimmy Olsson, Francois Roueff. “Posterior consistency for partially observed Markov models”. Journal of Stochastic Processes and their Applications, 130 (2): 733-759. 2020.
  48. Stephan Clémençon, Gabriela Ciolek, Patrice Bertail. “Statistical Learning Based On Markovian Data: Maximal Deviation Inequalities and Learning Rates”.  In the Annals of Mathematics and Artificial Intelligence, 88: 735–757. 2020.
  49. Mathieu Fontaine, Roland Badeau, Antoine Liutkus. “Separation of Alpha-Stable Random Vectors”. Signal Processing, 2020.
  50. Louis Faury, Marc Abeille, Clément Calauzènes, Olivier Fercoq. “Improved Optimistic Algorithms for Logistic Bandits“.  37th International Conference on Machine Learning (ICML), 2020, PMLR 119, pages 5598-5607
  51. François Roueff, Rainer von Sachs. “Time-frequency analysis of locally stationary Hawkes processes”. Bernoulli, Bernoulli Society for Mathematical Statistics and Probability, 2019, 25 (2), pp.1355-1385.
  52. Jean Paul Barddal, Fabrício Enembreck, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer. “Merit-guided dynamic feature selection filter for data streams”. Expert Syst. Appl. 116: 227-242. 2019.
  53. Rodrigo Fernandes de Mello, Yule Vaz, Carlos Henrique Grossi Ferreira, Albert Bifet. “On learning guarantees to unsupervised concept drift detection on data streams”. Expert Syst. Appl. 117: 90-102, 2019.
  54. Robert Anderson, Yun Sing Koh, Gillian Dobbie, Albert Bifet. “Recurring concept meta-learning for evolving data streams”. Expert Syst. Appl. 138, 2019.
  55. Abhik Ray, Lawrence B. Holder, Albert Bifet. “Efficient frequent subgraph mining on large streaming graphs”. Intell. Data Anal. 23(1): 103-132, 2019.
  56. Jean Paul Barddal, Fabrício Enembreck, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer. “Boosting decision stumps for dynamic feature selection on data streams.” Inf. Syst. 83: 13-29, 2019.
  57. Heitor Murilo Gomes, Jesse Read, Albert Bifet. “Streaming Random Patches for Evolving Data Stream Classification”. IEEE International Conference on Data Mining, ICDM, 2019.
  58. Luis Eduardo Boiko Ferreira, Heitor Murilo Gomes, Albert Bifet, Luiz S. Oliveira. “Adaptive Random Forests with Resampling for Imbalanced data Streams”. IJCNN, 2019.
  59. Minh Huong Le Nguyen, Heitor Murilo Gomes, Albert Bifet. “Semi-supervised Learning over Streaming Data using MOA”. IEEE Big Data Conference, 2019.
  60. Heitor Murilo Gomes, Rodrigo Mello, Bernhard Pfahringer, Albert Bifet. “Feature Scoring using Tree-Based Ensembles for Evolving Data Streams”. IEEE Big Data Conference, 2019.
  61. Dihia Boulegane, Albert Bifet, Giyyarpuram Madhusudan. “Arbitrated Dynamic Ensemble with Abstaining and Diversity Forecasting ensembles on Data Streams”. IEEE Big Data Conference, 2019.
  62. Guillaume Staerman, Pavlo Mozharovskyi, Stephan Clémençon, Florence d’Alché-Buc. “Functional Isolation Forest”. Proceedings of ACML, 2019.
  63. Edouard Pineau, Sebastien Razakarivony, Thomas Bonald. “Seq2VAR: multivariate time series representation with relational neural networks and linear autoregressive model”. AALTD workshop, ECML/PKDD, 2019.
  64. Patrice Bertail, François Portier. “Rademacher complexity for Markov chains: Applications to kernel smoothing and Metropolis–Hastings”.  Bernoulli, 25(4B): 3912-3938. 2019.
  65. Kamélia Daudel, Randal Douc, François Portier, François Roueff. “The f-divergence expectation iteration scheme”. 2019. ⟨hal-02298857⟩
  66. Amaury Durand, François Roueff. “Spectral representations of weakly stationary processes valued in a separable Hilbert space: a survey with applications on functional time series”. 2019. ⟨hal-02318267v2⟩

Exploitation à grande échelle de données partiellement étiquetées et hétérogènes (axe de recherche n°2)

  1. Pierre-Henri Paris, Fabian M. Suchanek.  “Non-named entities – the silent majority”. Extended Semantic Web Conference, ESWC 2021.
  2. Dimitri Bouche, Marianne Clausel, François Roueff, Florence d’Alché-Buc. “Nonlinear Functional Output Regression: a Dictionary Approach”. The 24th International Conference on Artificial Intelligence and Statistics, AISTATS 2021.
  3. Lihu Chen, Gaël Varoquaux, Fabian M. Suchanek. “A Lightweight Neural Model for Biomedical Entity Linking”. AAAI Conference on Artificial Intelligence, AAAI 2021.
  4. Gerhard Weikum, Luna Dong, Simon Razniewski, Fabian M. Suchanek. “Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases”. Foundations and Trends in Databases 2021.
  5. Thomas Pellissier Tanon, Fabian M. Suchanek. “Neural Knowledge Base Repairs”. Extended Semantic Web Conference, ESWC 2021.
  6. Geoffroy Peeters, Gael Richard. “Deep Learning for Audio and Music”. Multi-faceted Deep Learning: Models and Data, Springer, 2021.
  7. Ondrej Cifka, Umut Simsekli, Gaël Richard. “Groove2Groove: One-Shot Music Style Transfer with Supervision from Synthetic Data”. IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 2638-2650, 2020.
  8. Antoine Liutkus, Ondřej Cífka, Shih-Lun Wu, Umut Şimşekli, Yi-Hsuan Yang, Gaël Richard. “Relative Positional Encoding for Transformers with Linear Complexity.” 38th International Conference on Machine Learning, ICML 2021.
  9. Giorgia Cantisani, Alexey Ozerov, Slim Essid, Gael Richard. « User-guided one-shot deep model adaptation for music source separation”. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2021.
  10. Alp Yurtsever, Joel A. Tropp , Olivier Fercoq , Madeleine Udell , and Volkan Cevher. “Scalable Semidefinite Programming”. SIAM Journal on Mathematics of Data Science, 3(1), 171–200. 2021.
  11. Enrico Fini, Enver Sangineto, Stéphane Lathuilière, Zhun Zhong, Moin Nabi, Elisa Ricci. “A Unified Objective for Novel Class Discovery”. International Conference on Computer Vision, ICCV 2021.
  12. Javier Nistal, Cyran Aouameur, Stefan Lattner, Gaël Richard. “VQCPC-GAN: Variable-Length Adversarial Audio Synthesis Using Vector-Quantized Contrastive Predictive Coding”. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2021.
  13. Pierfrancesco Ardino, Marco De Nadai, Bruno Lepri, Elisa Ricci, Stéphane Lathuilière. “Click to Move: Controlling Video Generation with Sparse Motion”. International Conference on Computer Vision, ICCV 2021.
  14. Willi Menapace, Stephane Lathuiliere, Sergey Tulyakov, Aliaksandr Siarohin, Elisa Ricci. “Playable Video Generation ». Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2021.
  15. Sylvain Guy, Stéphane Lathuilière, Pablo Mesejo, Radu Horaud. “Learning Visual Voice Activity Detection with an Automatically Annotated Dataset”. International Conference on Pattern Recognition, ICPR 2021.
  16. Giorgia Cantisani, Slim Essid, Gael Richard. “Neuro-Steered Music Source Separation With EEG-Based Auditory Attention Decoding And Contrastive-NMF”. Proc. of International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021.
  17. Laure Pretet, Gael Richard, Geoffroy Peeters. “Cross-Modal Music-Video Recommendation: A Study of Design Choices.” Special Session of the International Joint Conference on Neural Networks, IJCNN 2021.
  18. Ondřej Cífka, Alexey Ozerov, Umut Şimşekli, Gael Richard. “Self-Supervised VQ-VAE for One-Shot Music Style Transfer”. Proc. of International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021.
  19. Patrice Bertail, Stephan Clémençon, Yannick Guyonvarch, Nathan Noiry. “Learning from Biased Data: A Semi-Parametric Approach”.  Proceedings of the 38th International Conference on Machine Learning, PMLR 139:803-812, ICML 2021.
  20. Javier Nistal, Stefan Lattner, Gaël Richard. “Comparing representations for audio synthesis using generative adversarial networks”. Proc. of the 28th European Signal Processing Conference, EUSIPCO2020, 2021.
  21. Guillaume Delorme, Yihong Xu, Stéphane Lathuiliére, Radu Horaud, Xavier Alameda-Pineda. « CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-Identification”.  2020 25th International Conference on Pattern Recognition, ICPR 2021.
  22. Aliaksandr Siarohin, Subhankar Roy, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, Nicu Sebe. « Motion-supervised Co-Part Segmentation”. 2020 25th International Conference on Pattern Recognition, pp. 9650-9657, ICPR 2021.
  23. Robin Vogel, Stéphan Clémençon. “A Multiclass Classification Approach to Label Ranking”. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:1421-1430, AISTATS 2020.
  24. Cristiano Saltori, Stéphane Lathuilière, Nicu Sebe, Elisa Ricci, Fabio Galasso. « SF-UDA3D: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection ».  International Conference on 3D Vision 3DV 2020.
  25. Willi Menapace, Stéphane Lathuilière, Elisa Ricci. « Learning to Cluster Under Domain Shift « . European Conference on Computer Vision, ECCV 2020.
  26. Enrico Fini, Stéphane Lathuilière, Enver Sangineto, Moin Nabi, Elisa Ricci. « Online Continual Learning under Extreme Memory Constraints ». European Conference on Computer Vision, ECCV 2020.
  27. Guillaume Doras, Geoffroy Peeters. “A Prototypical Triplet Loss for Cover Detection”. The 45th International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020.
  28.  Stéphan Clémençon, Robin Vogel.  “A Multiclass Classification Approach to Label Ranking”. Proceedings of AISTATS, 2020.
  29. Gabriel Meseguer-Brocal, Alice Cohen-Hadria, Geoffroy Peeters. “Creating DALI, a Large Dataset of Synchronized Audio, Lyrics, and Notes”. Transactions of the International Society for Music Information Retrieval, 3(1), 55–67. 2020.
  30. Javier Nistal, Stefan Lattner, Gaël Richard. “DrumGAN: Synthesis of Drum Sounds With Timbral Feature Condition-ing Using Generative Adversarial Networks”. Proc. of the International Society for Music Information Retrieval, ISMIR 2020 (preprint).
  31. Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gaël Richard, Florence d’Alché-Buc. »Multilingual Lyrics-to-Audio alignment ». Proc. of the International Society for Music Information Retrieval, ISMIR 2020.
  32. Karim Ibrahim, Elena Epure, Geoffroy Peeters, Gaël Richard. « Should we consider the users in contex-tual music auto-tagging models? » in Proc. of the International Society for Music Information Retrieval, ISMIR 2020.
  33. Catherine Taleb, Laurence Likforman-Sulem, Chafic Mokbel, Maha Khachab. “Detection of Parkinson’s disease from handwriting using deep learning: a comparative study”. Evolutionary Intelligence, 2020.
  34. Karim Ibrahim, Elena Epure, Geoffroy Peeters, Gaël Richard. « Confidence-based Weighted Loss for Multi-label Classification with Missing Labels. » The 2020 International Conference on Multimedia Retrieval, ICMR 2020.
  35. Ahmet Alacaoglu, Olivier Fercoq, Volkan Cevher. “Random extrapolation for primal-dual coordinate descent”. Proceedings of the 37th International Conference on Machine Learning, PMLR 119:191-201, 2020.
  36. Enguerrand Gentet, Bertrand David, Sebastien Denjean, Gaël Richard, Vincent Roussarie. « Speech intelligibility enhancement by equalization for in-car applications ».Proc. of International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020.
  37. Laure Prétet, Gael Richard, Geoffroy Peeters. « Learning to rank music tracks using triplet loss ». Proc. of International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020
  38. Andrea Vaglio, Romain Hennequin, Manuel Moussallam, Gael Richard, Florence d’Alché-Buc. « Audio-Based detection of explicit content in music » in Proc. of International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020.
  39. Kilian Schulze-Forster, Clément Doire, Gael Richard, Roland Badeau. « Joint phoneme alignment and text-informed speech separation on highly corrupted speech ». Proc. of International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020.
  40. Karim Ibrahim, Jimena Royo-Letelier, Elena Epure, Geoffroy Peeters, Gael Richard. « Audio-Based Auto-Tagging with contextual tags for music ». Proc. of International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020.
  41. The-Phuc Nguyen, Stéphane Lathuilière, Elisa Ricci. « Multi-Domain Image-to-Image Translation with Adaptive Inference Graph”. International Conference on Pattern Recognition, ICPR 2020.
  42. Gerhard Weikum, Johannes Hoffart, Fabian M. Suchanek. “Knowledge Harvesting: Achievements and Challenges”. Book chapter in Springer: Computing and Software Science, LNCS 2019.
  43. Olivier Fercoq, Pascal Bianchi. “A Coordinate Descent Primal-Dual Algorithm with Large Step Size and Possibly Non Separable Functions”. SIAM Journal on Optimization, 29(1), pp. 100-134, 2019.
  44. Olivier Fercoq, Zheng Qu. “Restarting the accelerated coordinate descent method with a rough strong convexity estimate”. Computational Optimization and Applications, 2019.
  45. Olivier Fercoq, Zheng Qu. “Adaptive restart of accelerated gradient methods under local quadratic growth condition”.  IMA Journal of Numerical Analysis, 2019.
  46. Luis Gasco, Chloé Clavel, Cesar Asensio, Guillermo de Arcasa. “Beyond sound level monitoring: Exploitation of social media to gather citizens subjective response to noise”. Science of The Total Environment, 658, pp.69-79. 2019.
  47. Celine Brouard, Antoine Bassé, Florence d’Alché-Buc, Juho Rousu. “Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models”. Metabolites, MDPI 2019.
  48. Pierre Laforgue, Florence d’Alché-Buc, Stéphan Clémençon. “Autoencoding any Data through Kernel Autoencoders”. AISTATS, 2019.
  49. Fabian M. Suchanek, Jonathan Lajus, Armand Boschin, Gerhard Weikum. “Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases”.  Invited paper at the Reasoning Web Summer School, RW 2019.
  50. Arnaud Soulet, Fabian M. Suchanek. “Anytime Large-Scale Analytics of Linked Open Data”. Full paper at the International Semantic Web Conference, ISWC 2019.
  51. Thomas Pellissier Tanon, Fabian M. Suchanek. “Querying the Edit History of Wikidata”. Demo at the Extended Semantic Web Conference, SWC 2019.
  52. Thomas Pellissier Tanon, Camille Bourgaux, Fabian M. Suchanek. “Learning How to Correct a Knowledge Base from the Edit History”. Full paper at the The Web Conference, WWW 2019.
  53. Eugene Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi. “Safe Grid Search with Optimal Complexity”. Proc. of the International Conference on Machine Learning, 2019.
  54. Alexandre Garcia, Pierre Colombo, Florence d’Alché-Buc, Slim Essid, Chloé Clavel. “From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining”.   EMNLP 2019.
  55. Léo Hemamou, Ghazi Felhi, Vincent Vandenbussche, Jean-Claude Martin, Chloé Clavel.  “HireNet: a Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews”. AAAI 2019.
  56. Robin Vogel, Aurélien Bellet, Stephan Clémençon, Ons Jelassi, Guillaume Papa. “Trade-offs in Large-Scale Distributed Tuplewise Estimation and Learning”. Proceedings of ECML, 2019.
  57. Stéphan Clémençon, Mastane Achab, Anna Korba. “Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach”. Proceedings of ALT, 2019.
  58. Bertrand Charpentier, Thomas Bonald. “Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering”. IJCAI 2019.
  59. Pierre Laforgue, Alex Lambert, Luc Motte, Florence d’Alché-Buc. “On the Dualization of Operator-Valued Kernel Machines”. CoRR 2019.
  60. Alexandre Garcia, Slim Essid, Florence d’Alché-Buc, Chloé Clavel. “A multimodal movie review corpus for fine-grained opinion mining”. CoRR 2019.

Apprentissage statistique au service d’une prise de décision fiable et robuste (axe de recherche n°3)

  1. Holger Drees, Anne Sabourin. “Principal component analysis for multivariate extremes”. Electronic Journal of Statistics, 15(1): 908-943. 2021.
  2. Fabien Collas, Ekhine Irurozki.  “Concentric Mallows mixtures for top-k rankings: sampling and identifiability”. Proceedings of the 38th International Conference on Machine Learning, PMLR 139:2079-2088, ICML 2021.
  3. Nedeljko Radulović, Albert Bifet, Fabian M. Suchanek. “Confident Interpretations of Black Box Classifiers”. International Joint Conference on Neural Networks, IJCNN 2021.
  4. Anas Barakat, Pascal Bianchi. “Convergence and Dynamical Behavior of the ADAM Algorithm for Nonconvex Stochastic Optimization”. Society for Industrial and Applied Mathematics journal on Optimization, 31(1), 244–274. SIAM 2021. (In addition, research axis No 4).
  5. Anas Barakat, Pascal Bianchi, Walid Hachem, Sholom Schechtman. “Stochastic optimization with momentum: convergence, fluctuations, and traps avoidance”. Electronic Journal of Statistics 15 (2), 3892-3947. 2021. (In addition, research axis No 4).
  6. Kevin Elgui, Pacal Bianchi, Olivier Isson, Francois Portier, Renaud Marty. “Metric Learning for Fingerprint RSSI-Localization“.  2020 IEEE/ION Position, Location and Navigation Symposium. PLANS 2020.
  7. Hamid Jalalzai, Pierre Colombo, Chloé Clavel, Eric Gaussier, Giovanna Varni, Emmanuel Vignon, Anne Sabourin. “Heavy-tailed Representations, Text Polarity Classification & Data Augmentation”. Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020.
  8. Louis Faury, Marc Abeille, Clément Calauzènes, Olivier Fercoq. “Improved Optimistic Algorithms for Logistic Bandits“. Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3052-3060, 2020.
  9. Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d’Alché-Buc, James Eagan, James Eagan, Pavlo Mozharovskyi , Jayneel Parekh.  “Identifying the « Right » Level of Explanation in a Given Situation”.  Proceedings of ECAI 2020.
  10. Robin Vogel, Mastane Achab, Stéphan Clémençon, Charles Tillier. “Weighted Empirical Risk Minimization: Transfer Learning based on Importance Sampling”. 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Proceedings of ESANN 2020.
  11. Guillaume Staerman, Pavlo Mozharovskyi, Stéphan Clémençon. “The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure”. Proceedings of AISTATS 2020.
  12. Robin Vogel, Mastane Achab, Stéphan Clémençona, Charles Tillier. “Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling”. Proceedings of ICMA 2020.
  13. Anas Barakat, Pascal Bianchi. « Convergence Rates of a Momentum Algorithm with Bounded Adaptive Step Size for Non-Convex Optimization.” 12th Asian Conference on Machine Learning, PMLR 129:225-240, 2020. (In addition, research axis No 4).
  14. Kimia Nadjahi, Valentin De Bortoli, Alain Durmus, Roland Badeau, Umut Şimşekli. “Approximate Bayesian Computation with the Sliced-Wasserstein Distance”.  2020 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5470-5474, ICASSP 2020.
  15. 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”. Proceedings of ECAI 2020.
  16. Kevin Elgui, Pascal Bianchi, François Portier, Olivier Isson. “Learning methods for RSSI-based geolocation: A comparative study“. Pervasive and Mobile Computing, 67, 101199, 2020.
  17. Olivier Fercoq. “A generic coordinate descent solver for nonsmooth convex optimization”. Optimization Methods and Software, Taylor & Francis, pp.1-21. 2019.
  18. Olivier Fercoq, Peter Richtárik. “Smooth Minimization of Nonsmooth Functions with Parallel Coordinate Descent Methods”. Modeling and Optimization: Theory and Applications, pp.57-96, 2019.
  19. Catherine Taleb, Maha Khachab, Chafic Mokbel, Laurence Likforman-Sulem. “Visual Representation of Online Handwriting Time Series for Deep Learning Parkinson’s Disease Detection”. 2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019.
  20. Catherine Taleb, Laurence Likforman-Sulem, Chafic Mokbel. “Improving Deep Learning Parkinson’s Disease Detection Through Data Augmentation Training”. Pattern Recognition and Artificial Intelligence. Communications in Computer and Information Science, vol 1144. MedPRAI 2019.
  21. Mael Chiapino, Stéphan Clémençon, Vincent Feuillard, Anne Sabourin. “A multivariate extreme value theory approach to anomaly clustering and visualization”. Computational Statistics 2019.
  22. Francesco Locatello, Alp Yurtsever, Olivier Fercoq, Volkan Cevher. “Stochastic Conditional Gradient Method for Composite Convex Minimization”. Neural Information Processing Systems 2019.
  23. Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara, Volkan Cevher. “Almost surely constrained convex optimization”. International Conference on Machine Learning, 2019.
  24. Alp Yurtsever, Olivier Fercoq, Volkan Cevher. “A Conditional Gradient-Based Augmented Lagrangian Framework”. International Conference on Machine Learning, 2019.
  25. Kevin Elgui, Pascal Bianchi, François Portier, Olivier Isson. “Learning Methods for RSSI-based Geolocation: A Comparative Study”. 27th European Signal Processing Conference, EUSIPCO 2019.
  26. Romain Brault, Alex Lambert, Zoltán Szabó, Maxime Sangnier, Florence d’Alché-Buc. “Infinite Task Learning in RKHSs“. AISTATS 2019.
  27. Naman Singh Negi, Naman Negi, Ons Jelassi, Stéphan Clémençon, Sebastian Fischmeister. “A LSTM Approach to Detection of Autonomous Vehicle Hijacking”. 5th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2019.
  28. Stéphan Clémençon, Robin Vogel. “On Tree-based Methods for Similarity Learning”. LOD 2019.
  29. Pierre Laforgue, Stephan Clémençon, Patrice Bertail. “On Medians of (Randomized) Pairwise Means“. Proceedings of the 36th International Conference on Machine Learning, PMLR 97:1272-1281. ICML 2019.
  30. Othmane Sebbouh, Nidham Gazagnadou, Samy Jelassi, Francis Bach, Robert Gower. “Towards closing the gap between the theory and practice of SVRG”. Conference on Neural Information Processing Systems, NeurIPS 2019.
  31. Robert M. Gower, Dmitry Kovalev, Felix Lieder, Peter Richtárik. “RSN: Randomized Subspace Newton”.  Conference on Neural Information Processing Systems, NeurIPS 2019.
  32. Nidham Gazagnadou, Robert M. Gower, Joseph Salmon. “Optimal mini-batch and step sizes for SAGA”. ICML 2019.
  33. Robert Gower, Nicolas Loizou, Xun Qian, Alibek Sailanbayev, Egor Shulgin, Peter Richtárik. “SGD: General Analysis and Improved Rates”. 36th International Conference on Machine Learning, PMLR 97:5200-5209, 2019.
  34. Léo Hemamou, Ghazi Felhi, Jean-Claude Martin, Chloé Clavel. “Slices of Attention in Asynchronous Video Job Interviews“.8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019.
  35. Alireza Fallah, Mert Gurbuzbalaban, Asuman Ozdaglar, Umut Simsekli, Lingjiong Zhu. “Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks”. CoRR 2019.

Apprentissage dans un environnement dynamique (axe de recherche n°4)

  1. Anas Barakat, Pascal Bianchi.  “Convergence and Dynamical Behavior of the ADAM Algorithm for Nonconvex Stochastic Optimization”. J. Optim., 31(1), 244–274. 2021.
  2. Pierre Colombo, Emile Chapuis, Matthieu Labeau, Chloé Clavel. “Code-switched inspired losses for spoken dialog representations”. EMNLP 2021.
  3. Pierre Colombo, Chloe Clavel, Pablo Piantanida. “A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations“. ACL 2021.
  4. Gaël Guibon, Matthieu Labeau, Hélène Flamein, Luce Lefeuvre, Chloé Clavel. “Méta-apprentissage : classification de messages en catégories émotionnelles inconnues en entraînement“. TALN 2021.
  5. Gaël Guibon, Matthieu Labeau, Hélène Flamein, Luce Lefeuvre, Chloé Clavel. “Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional Categories”. Workshop MetaNLP @ACL – Meta Learning and Its Applications to Natural Language Processing, 2021.
  6. Ekhine Irurozki, Manuel López-Ibáñez.  “Unbalanced Mallows Models for Optimizing Expensive Black-Box Permutation Problems”. The Genetic and Evolutionary Computation Conference GECCO21, 2021.
  7. Tanvi Dinkar, Pierre Colombo, Matthieu Labeau, Chloé Clavel. “The importance of fillers for text representations of speech transcripts”. EMNLP 2020.
  8. Emile Chapuis, Pierre Colombo, Matteo Manica, Matthieu Labeau, Chloé Clavel.  “Hierarchical Pre-training for Sequence Labelling in Spoken Dialog”. EMNLP, 2636–2648. 2020.
  9. Yi Ren, Shangmin Guo, Matthieu Labeau, Shay B. Cohen, Simon Kirby. “Compositional languages emerge in a neural iterated learning model”. ICLR 2020.
  10. Anas Barakat, Pascal Bianchi. “Convergence Analysis of a Momentum Algorithm with Adaptive Step Size for Nonconvex Optimization”. ICLR 2020.
  11. Atef Ben Youssef, Giovanna Varni, Slim Essid, Chloé Clavel. “On-the-fly Detection of User Engagement Decrease in Spontaneous Human-Robot Interaction”. International Journal of Social Robotics, 2019.
  12. Atef Ben Youssef, Chloé Clavel, Slim Essid. “Early Detection of User Engagement Breakdown in Spontaneous Human-Humanoid Interaction”. IEEE Transactions on Affective Computing, 2019.
  13. Anas Barakat, Pascal Bianchi. “Convergence Analysis of a Momentum Algorithm with Adaptive Step Size for Non Convex Optimization”. 11th Annual Workshop on Optimization for Machine Learning, OPT 2019.