Publications

2025

• [de Santis et al. 2025] M. de Santis, J. Patracone, F. Rinaldi, S. Salzo, M. Schmidt, S. Venturini, Relax and penalize: a new bilevel approach to mixed-binary hyperparameter optimization, Transactions on Machine Learning Research Journal, (2025)

2024

• [Ferbach et al. 2024] D. Ferbach, Q. Bertrand, A. Bose, G. Gidel, Self-Consuming Generative Models with Curated Data Provably Optimize Human Preferences, NeurIPS, pp. 1-27, Vancouver (BC), Canada (2024) - CORE Ranking : A*
• [Ali Banna et al. 2024a] F. Ali Banna, J. P. Colombier, R. Emonet, M. Sebban, Physics-informed Machine Learning for Better Understanding Laser-Matter Interaction, The 36th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2024), pp. 7, Herndon, VA, United States (2024) - CORE Ranking : B
• [Marouani et al. 2024] S. Marouani, K. Singh, B. Jeudy, A. Bradai, A. Habrard, Advanced Traffic Engineering in WAN Using Graph Attention Networks, The 20th International Conference on Wireless and Mobile Computing, Networking and Communications, Wimob 2024, Paris, France (2024) - CORE Ranking : B
• [Soto et al. 2024] E. Soto, R. Emonet, M. Sebban, Unsupervised Learning and Effective Complexity: introducing JPG and Neural Sophistication, International Conference on Tools with Artificial Intelligence (ICTAI), Herndon, United States (2024) - CORE Ranking : B
• [Colombier et al. 2024] J. P. Colombier, E. Brandao, A. Nakhoul, F. Ali Banna, R. Emonet, A. Habrard, F. Jacquenet, F. Garrelie, M. Sebban, Deciphering the complexity behind laser-induced selforganized nanopatterns, 17th International Conference on Laser Ablation (COLA 2024), Hersonissos, Greece (2024)
• [Girault et al. 2024] B. Girault, R. Emonet, A. Habrard, J. Patracone, M. Sebban, Approximation Error of Sobolev Regular Functions with tanh Neural Networks: Theoretical Impact on PINNs, 2024 Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2024), Vilnius, Lithuania (2024) - CORE Ranking : A
• [Patracone et al. 2024a] J. Patracone, P. Viallard, E. Morvant, G. Gasso, A. Habrard, S. Canu, A Theoretically Grounded Extension of Universal Attacks from the Attacker's Viewpoint, ECML PKDD 2024 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 1-27, Vilnius, Lithuania (2024) - CORE Ranking : A
• [Patracone et al. 2024b] J. Patracone, L. Anquetil, Y. Liu, G. Gasso, S. Canu, Linear Modeling of the Adversarial Noise Space, ECML PKDD 2024 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Vilnius, Lithuania (2024) - CORE Ranking : A
• [Chaki et al. 2024] S. Chaki, Z. Baltaci, E. Vincent, R. Emonet, F. Vial-Bonacci, C. Bahier-Porte, M. Aubry, T. Fournel, Historical Printed Ornaments: Dataset and Tasks, ICDAR 2024 - International Conference on Document Analysis and Recognition, pp. 251-270, Athens, Greece (2024) - CORE Ranking : A
• [Atbir et al. 2024] H. Atbir, F. Cherfaoui, G. Metzler, E. Morvant, P. Viallard, Une borne PAC-Bayésienne sur une mesure de risque pour l'apprentissage équitable, CAP 2024 - Conférence sur l'Apprentissage Automatique, Lille, France (2024)
• [Azorin-Lopez et al. 2024] J. Azorin-Lopez, M. Sebban, N. Garcia-d'Urso, A. Habrard, A. Fuster-Guillo, Generative shape deformation with optimal transport using learned transformations, International Joint Conference on Neural Networks (IJCNN), YOKOHAMA, Japan (2024) - CORE Ranking : B
• [Mitarchuk et al. 2024b] V. Mitarchuk, C. Lacroce, R. Eyraud, R. Emonet, A. Habrard, G. Rabusseau, Length Independent PAC-Bayes Bounds for Simple RNNs, AISTATS 2024 - 27th International Conference on Artificial Intelligence and Statistics, Valence, Spain (2024) - CORE Ranking : A
• [Viallard et al. 2024b] P. Viallard, R. Emonet, A. Habrard, E. Morvant, V. Zantedeschi, Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures, AISTATS 2024 - 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain (2024) - CORE Ranking : A
• [Ali Banna et al. 2024b] F. Ali Banna, R. Emonet, A. Rudenko, M. Sebban, J. P. Colombier, Predicting laser energy absorption on nanostructured surfaces with deep learning, Machine Learning in Photonics, pp. 74, Strasbourg, France (2024)
• [Robissout et al. 2024] D. Robissout, L. Bossuet, A. Habrard, Scoring the predictions: a way to improve profiling side-channel attacks, Journal of Cryptographic Engineering, (2024) - IF : 1.5 (Q2)
• [Bilko et al. 2024a] K. Bilko, R. Alía, M. Barbero, S. Girard, Y. Aguiar, M. Cecchetto, C. Belanger-Champagne, S. Danzeca, W. Hajdas, A. Hands, P. Holgado, Y. Garcia, A. Maestre, D. Prelipcean, F. Ravotti, M. Sebban, Mixed-field Radiation Monitoring and Beam Characterisation Through Silicon Diode Detectors, IEEE Transactions on Nuclear Science, vol. 71(4), pp. 777-784 (2024) - IF : 1.9 (Q1)
• [Bilko et al. 2024b] K. Bilko, R. Alía, A. Constantino, A. Coronetti, S. Danzeca, M. Delrieux, N. Emriskova, M. Fraser, S. Girard, E. Johnson, M. Sebban, F. Ravotti, A. Waets, CHARM High-energy Ions for Micro Electronics Reliability Assurance (CHIMERA), IEEE Transactions on Nuclear Science, pp. 1-1 (2024) - IF : 1.9 (Q1)
• [Mitarchuk and Eyraud 2024] V. Mitarchuk, R. Eyraud, A Theoretical Analysis of the Incremental Counting Ability of LSTM in Finite Precision, LearnAut workshop 2024, Tallinn, Estonia (2024)
• [Mitarchuk et al. 2024a] V. Mitarchuk, R. Emonet, R. Eyraud, A. Habrard, On the theoretical limit of gradient descent for Simple Recurrent Neural Networks with finite precision, Transactions on Machine Learning Research Journal, (2024)
• [Viallard et al. 2024a] P. Viallard, P. Germain, A. Habrard, E. Morvant, A General Framework for the Practical Disintegration of PAC-Bayesian Bounds, Machine Learning, vol. 113(2), pp. 519-604 (2024) - IF : 4.3 (Q2)

2023

• [Leteno et al. 2023] T. Leteno, A. Gourru, C. Laclau, R. Emonet, C. Gravier, Fair Text Classification with Wasserstein Independence, 2023 Conference on Empirical Methods in Natural Language Processing, pp. 15790-15803, Singapore, Singapore (2023) - CORE Ranking : A*
• [Biłko et al. 2023a] K. Biłko, R. Alía, S. Girard, M. Barbero, M. Cecchetto, C. Belanger-Champagne, M. Brucoli, S. Danzeca, A. Hands, P. Holgado, Y. Garcia, A. Maestre, M. Sebban, M. Widorski, Ultra-large Silicon Diode for Characterising Low-intensity Radiation Environments, IEEE Transactions on Nuclear Science, vol. 71(4), pp. 770-776 (2023) - IF : 1.9 (Q1)
• [Llavata et al. 2023] D. Llavata, E. Cagli, R. Eyraud, V. Grosso, L. Bossuet, Deep stacking ensemble learning applied to profiling side-channel attacks, Smart Card Research and Advanced Application Conference - CARDIS, pp. 235–255, Amsterdam, Netherlands (2023) - CORE Ranking : C
• [Biłko et al. 2023b] K. Biłko, R. Alía, Y. Aguiar, S. Danzeca, D. Francesca, S. Gilardoni, S. Girard, D. Ricci, M. Sebban, S. Uznanski, Radiation environment in the Large Hadron Collider during the 2022 restart and related RHA implications, IEEE Transactions on Nuclear Science, vol. 71(4), pp. 607-617 (2023) - IF : 1.9 (Q1)
• [Brandao et al. 2023b] E. Brandao, S. Duffner, R. Emonet, A. Habrard, F. Jacquenet, M. Sebban, Is My Neural Net Driven by the MDL Principle?, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 173-189, Turin, France (2023) - CORE Ranking : A
• [Eyraud et al. 2023b] R. Eyraud, D. Lambert, B. Tahri, A. Gaffarov, M. Cabanne, J. Heinz, C. Shibata, TAYSIR Competition: Transformer+rnn: Algorithms to Yield Simple and Interpretable Representations, International Conference on Grammatical Inference, pp. 275-290, Rabat, Morocco (2023)
• [Biłko et al. 2023d] K. Biłko, R. Garcia Alia, S. Girard, M. Sebban, Overview of total ionizing dose levels in the Large Hadron Collider during 2022 restart, 14th International Particle Accelerator Conference, Geneva, Switzerland (2023)
• [Bouniot et al. 2023a] Q. Bouniot, R. Audigier, A. Loesch, A. Habrard, Proposal-contrastive pretraining for object detection from fewer data, ICLR 2023 - The Eleventh International Conference on Learning Representations, pp. https://openreview.net/forum?id=gm0VZ-h-hPy, Kigali, Rwanda (2023) - CORE Ranking : A*
• [Brandao et al. 2023a] E. Brandao, A. Nakhoul, S. Duffner, R. Emonet, F. Garrelie, A. Habrard, F. Jacquenet, F. Pigeon, M. Sebban, J. P. Colombier, Learning Complexity to Guide Light-Induced Self-Organized Nanopatterns, Physical Review Letters, vol. 130(), pp. 226201 (2023) - IF : 8.1 (Q1)
• [Baksheeva et al. 2023] V. Baksheeva, V. Tiulina, E. Iomdina, S. Petrov, O. Filippova, N. Kushnarevich, V. Tiuli, R. Eyraud, F. Devred, M. Serebryakova, N. Shebardina, D. Chistyakov, I. Senin, M. Serebryakova, P. Tsvetkov, E. Zernii, Tear nanoDSF Denaturation Profile Is Predictive of Glaucoma, International Journal of Molecular Sciences, vol. 24(7132), pp. 7132 (2023) - IF : 4.9 (Q1)
• [Biłko et al. 2023c] K. Biłko, R. Alía, D. Francesca, Y. Aguiar, S. Danzeca, S. Gilardoni, S. Girard, L. Esposito, M. Fraser, G. Mazzola, D. Ricci, M. Sebban, F. Velotti, CERN Super Proton Synchrotron Radiation Environment and Related Radiation Hardness Assurance Implications, IEEE Transactions on Nuclear Science, vol. 70(8), pp. 1606-1615 (2023) - IF : 1.9 (Q1)
• [Zaid et al. 2023] G. Zaid, L. Bossuet, M. Carbone, A. Habrard, A. Venelli, Conditional Variational AutoEncoder based on Stochastic Attacks, Workshop on Cryptographic Hardware and Embedded Systems, pp. 310-357, (2023) - CORE Ranking : A
• [Rußwurm et al. 2023] M. Rußwurm, N. Courty, R. Emonet, S. Lefèvre, D. Tuia, R. Tavenard, End-to-end learned early classification of time series for in-season crop type mapping, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 196(), pp. 445-456 (2023) - IF : 10.6 (Q1)
• [Bouniot et al. 2023b] Q. Bouniot, A. Loesch, A. Habrard, R. Audigier, Towards few-annotation learning for object detection: are transformer-based models more efficient ?, WACV2023 - 2023 IEEE-CVF Winter Conference on Applications of Computer Vision, pp. 75-84, Waikoloa, HI, United States (2023) - CORE Ranking : A
• [Eyraud et al. 2023a] R. Eyraud, S. Ayache, P. Tsvetkov, S. Kalidindi, V. Baksheeva, S. Boissonneau, C. Jiguet-Jiglaire, R. Appay, I. Nanni-Metellus, O. Chinot, F. Devred, E. Tabouret, Plasma nanoDSF Denaturation Profile at Baseline Is Predictive of Glioblastoma EGFR Status, Cancers, (2023) - IF : 4.5 (Q1)

2022

• [Kaloga et al. 2022] Y. Kaloga, P. Borgnat, A. Habrard, A Simple Way to Learn Metrics Between Attributed Graphs, Proceedings of the First Learning on Graphs Conference (LoG 2022),, Virtual Event (Republic of Korea), France (2022)
• [Mermillo-Blondin et al. 2022] R. Mermillo-Blondin, N. Dalloz, R. Emonet, N. Destouches, Optimisation muliticritère de couleurs plasmoniques pour les documents d'identité, NanoApp, Villeurbanne (France), France (2022)
• [Bouniot et al. 2022] Q. Bouniot, I. Redko, R. Audigier, A. Loesch, A. Habrard, Improving few-shot learning through multi-task representation learning theory, 17th European Conference on Computer Vision – ECCV 2022, pp. 435-452, Tel Aviv, Israel (2022) - CORE Ranking : A*
• [Colombier et al. 2022] J. P. Colombier, E. Brandao, A. Nakhoul, R. Emonet, F. Garrelie, A. Habrard, F. Jacquenet, F. Pigeon, A. Rudenko, M. Sebban, Stochasticity versus determinism in LIPSS formation, 10th International Workshop LIPSS, Orléans, France (2022)
• [Brandao et al. 2022] E. Brandao, J. P. Colombier, S. Duffner, R. Emonet, F. Garrelie, A. Habrard, F. Jacquenet, A. Nakhoul, M. Sebban, Learning PDE to Model Self-Organization of Matter, Entropy, vol. 24(8), (2022) - IF : 2.7 (Q2)
• [Jhuboo et al. 2022] R. Jhuboo, I. Redko, A. Guignandon, F. Peyrin, M. Sebban, Why do State-of-the-art Super-Resolution Methods not work well for Bone Microstructure CT Imaging?, EUSIPCO 2022, Belgrade, France (2022) - CORE Ranking : B
• [Borja-Borja et al. 2022] L. Borja-Borja, J. Azorin-Lopez, M. Saval-Calvo, A. Fuster-Guillo, M. Sebban, Architecture for Automatic Recognition of Group Activities Using Local Motions and Context, IEEE Access, vol. 10(), pp. 79874-79889 (2022) - IF : 3.9 (Q2)
• [Ma et al. 2022b] H. Ma, N. Dalloz, A. Habrard, M. Sebban, M. Hebert, N. Destouches, optimized laser induced colors and image multiplexing on plasmonic quasi-random metasurfaces using deep learning, 12th international conference on metamaterials, torremolinos, Spain (2022)
• [Ma et al. 2022c] R. Ma, N. Dalloz, A. Habrard, M. Sebban, M. Hebert, N. Destouches, Optimized laser-induced colors and image multiplexing on plasmonic quasi-random metasurfaces using deep learning, 12th International Conference on Metamaterials (META 2022), Torremolinos, Spain (2022)
• [Viallard et al. 2022] P. Viallard, R. Emonet, P. Germain, A. Habrard, E. Morvant, V. Zantedeschi, Intérêt des bornes désintégrées pour la généralisation avec des mesures de complexité, CAp 2022, Vannes, France (2022)
• [Viola et al. 2022] R. Viola, L. Gautheron, A. Habrard, M. Sebban, MetaAP: a meta-tree-based ranking algorithm optimizing the average precision from imbalanced data, Pattern Recognition Letters, vol. 161(), pp. 161-167 (2022) - IF : 5.1 (Q2)
• [Zantedeschi et al. 2022] V. Zantedeschi, P. Viallard, E. Morvant, R. Emonet, A. Habrard, P. Germain, B. Guedj, Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound, CAp 2022, Vannes, France (2022)
• [Ma et al. 2022a] H. Ma, N. Dalloz, A. Habrard, M. Sebban, F. Sterl, H. Giessen, M. Hebert, N. Destouches, Predicting Laser-Induced Colors of Random Plasmonic Metasurfaces and Optimizing Image Multiplexing Using Deep Learning, ACS Nano, vol. 16(6), pp. 9410-9419 (2022) - IF : 17.1 (Q1)
• [Marcotte et al. 2022] S. Marcotte, A. Barbe, R. Gribonval, T. Vayer, M. Sebban, P. Borgnat, P. Gonçalves, Fast Multiscale Diffusion on Graphs, ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, Singapore (2022) - CORE Ranking : B
• [Kerdoncuff et al. 2022] T. Kerdoncuff, M. Perrot, R. Emonet, M. Sebban, Optimal Tensor Transport, AAAI, Vancouver, Canada (2022) - CORE Ranking : A*
• [Waniek et al. 2022] M. Waniek, P. Holme, K. Farrahi, R. Emonet, M. Cebrian, T. Rahwan, Trading contact tracing efficiency for finding patient zero, Scientific Reports, vol. 12(1), pp. 22582 (2022) - IF : 4.6 (Q2)

2021

• [Viallard et al. 2021a] P. Viallard, G. Vidot, A. Habrard, E. Morvant, A PAC-Bayes Analysis of Adversarial Robustness, Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), pp. 1105, pp. 14421 - 14433, Virtual-only Conference, Australia (2021) - CORE Ranking : A*
• [Barbe et al. 2021] A. Barbe, P. Gonçalves, M. Sebban, P. Borgnat, R. Gribonval, T. Vayer, Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain Adaptation, ICTAI 2021 - 33rd IEEE International Conference on Tools with Artificial Intelligence, pp. 1-8, Virtual conference, France (2021) - CORE Ranking : B
• [Kaloga et al. 2021a] Y. Kaloga, P. Borgnat, S. Chepuri, P. Abry, A. Habrard, Variational graph autoencoders for multiview canonical correlation analysis, Signal Processing, vol. 188(), pp. 108182 (2021) - IF : 4.729 (Q2)
• [Rusu et al. 2021] A. Rusu, R. Emonet, K. Farrahi, Modelling digital and manual contact tracing for COVID-19. Are low uptakes and missed contacts deal-breakers?, PLoS ONE, vol. 16(11), pp. e0259969 (2021) - IF : 3.752 (Q2)
• [Azorin-Lopez et al. 2021] J. Azorin-Lopez, M. Sebban, A. Fuster-Guillo, M. Saval-Calvo, A. Habrard, Iterative multilinear optimization for planar model fitting under geometric constraints, PeerJ Computer Science, vol. 7(), pp. e691 (2021) - IF : 2.411 (Q2)
• [Viallard et al. 2021b] P. Viallard, P. Germain, A. Habrard, E. Morvant, Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound, ECML PKDD 2021, Bilbao, Spain (2021) - CORE Ranking : A
• [Zaid et al. 2021] G. Zaid, L. Bossuet, A. Habrard, A. Venelli, Efficiency through Diversity in Ensemble Models applied to Side-Channel Attacks, Workshop on Cryptographic Hardware and Embedded Systems, pp. 60-96, (2021) - CORE Ranking : A
• [Kaloga et al. 2021b] Y. Kaloga, P. Borgnat, S. Chepuri, P. Abry, A. Habrard, Multiview Variational Graph Autoencoders for Canonical Correlation Analysis, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5320-5324, Toronto, Canada (2021) - CORE Ranking : B
• [Robissout et al. 2021] D. Robissout, L. Bossuet, A. Habrard, V. Grosso, Improving Deep Learning Networks for Profiled Side-channel Analysis Using Performance Improvement Techniques, ACM Journal on Emerging Technologies in Computing Systems, vol. 17(3), pp. 1-30 (2021) - IF : 2.013 (Q3)
• [Viola et al. 2021] R. Viola, R. Emonet, A. Habrard, G. Metzler, S. Riou, M. Sebban, A Nearest Neighbor Algorithm for Imbalanced Classification, International Journal on Artificial Intelligence Tools, vol. 30(3), pp. 2150013 (2021) - IF : 1.059 (Q4)
• [Tsvetkov et al. 2021] P. Tsvetkov, R. Eyraud, S. Ayache, A. Bougaev, S. Malesinski, H. Benazha, S. Gorokhova, C. Buffat, C. Dehais, M. Sanson, F. Bielle, D. Figarella Branger, O. Chinot, E. Tabouret, F. Devred, An AI-Powered Blood Test to Detect Cancer Using NanoDSF, Cancers, vol. 13(6), pp. 1294 (2021) - IF : 6.575 (Q1)
• [Cerquides et al. 2021] J. Cerquides, R. Emonet, G. Picard, J. Rodriguez-Aguilar, Solving highly cyclic distributed optimization problems without busting the bank: a decimation-based approach, Logic Journal of the IGPL, vol. 29(1), pp. 72–95 (2021) - IF : 0.868 (Q2)
• [Kerdoncuff et al. 2021] T. Kerdoncuff, R. Emonet, M. Sebban, Sampled Gromov Wasserstein, Machine Learning, (2021) - IF : 5.414 (Q2)
• [Zantedeschi et al. 2021] V. Zantedeschi, P. Viallard, E. Morvant, R. Emonet, A. Habrard, P. Germain, B. Guedj, Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound, NeurIPS, Online, France (2021)

2020

• [Bouniot et al. 2020] Q. Bouniot, I. Redko, R. Audigier, A. Loesch, A. Habrard, Putting theory to work: from learning bounds to meta-learning algorithms, Workshop Meta-Learn@NeurIPS 2020, Vancouver (Virtual conference), Canada (2020)
• [Zaid et al. 2020a] G. Zaid, L. Bossuet, F. Dassance, A. Habrard, A. Venelli, Ranking Loss: Maximizing the Success Rate in Deep Learning Side-Channel Analysis, Workshop on Cryptographic Hardware and Embedded Systems, pp. 25-55, (2020) - CORE Ranking : A
• [Barbe et al. 2020b] A. Barbe, M. Sebban, P. Gonçalves, P. Borgnat, R. Gribonval, Transport Optimal entre Graphes exploitant la Diffusion de la Chaleur, CAP 2020 - Conférence sur l'Apprentissage Automatique, Vannes, France (2020)
• [Wang et al. 2020] Y. Wang, R. Emonet, E. Fromont, S. Malinowski, R. Tavenard, Adversarial regularization for explainable-by-design time series classification, ICTAI 2020 - 32th International Conference on Tools with Artificial Intelligence, pp. 1-9, online, Greece (2020) - CORE Ranking : B
• [Barbe et al. 2020a] A. Barbe, M. Sebban, P. Gonçalves, P. Borgnat, R. Gribonval, Graph Diffusion Wasserstein Distances, ECML PKDD 2020 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 1-16, Ghent, Belgium (2020) - CORE Ranking : A
• [Béthune et al. 2020] L. Béthune, Y. Kaloga, P. Borgnat, A. Garivier, A. Habrard, Hierarchical and Unsupervised Graph Representation Learning with Loukas’s Coarsening, Algorithms, vol. 13(9), pp. 206 (2020) - IF : 1.8 (Q2)
• [Eyraud and Ayache 2020] R. Eyraud, S. Ayache, Distillation of Weighted Automata from Recurrent Neural Networks using a Spectral Approach *, Machine Learning, (2020) - IF : 2.94 (Q2)
• [Gautheron et al. 2020b] L. Gautheron, P. Germain, A. Habrard, G. Metzler, E. Morvant, M. Sebban, V. Zantedeschi, Landmark-based Ensemble Learning with Random Fourier Features and Gradient Boosting, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Ghent, Belgium (2020) - CORE Ranking : A
• [Dhouib et al. 2020] S. Dhouib, I. Redko, T. Kerdoncuff, R. Emonet, M. Sebban, A Swiss Army Knife for Minimax Optimal Transport, Thirty-seventh International Conference on Machine Learning, Vienne, Austria (2020)
• [Duque et al. 2020] C. Duque, O. Alata, R. Emonet, H. Konik, A. C. Legrand, Mean oriented Riesz features for micro expression classification, Pattern Recognition Letters, vol. 135(), pp. 382-389 (2020) - IF : 3.756 (Q2)
• [Viola et al. 2020a] R. Viola, R. Emonet, A. Habrard, G. Metzler, M. Sebban, Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to deal with Imbalanced Data, IJCAI 2020, the 29th International Joint Conference on Artificial Intelligence, pp. 2155-2161, Yokohama, Japan (2020) - CORE Ranking : A*
• [Viola et al. 2020b] R. Viola, R. Emonet, A. Habrard, G. Metzler, M. Sebban, MLFP: Un algorithme d'apprentissage de métrique pour la classification de données déséquilibrées, Conférence sur l'Apprentissage automatique (CAp 2020), Vannes, France (2020)
• [Gautheron et al. 2020a] L. Gautheron, A. Habrard, E. Morvant, M. Sebban, Metric Learning from Imbalanced Data with Generalization Guarantees, Pattern Recognition Letters, vol. 133(), pp. 298-304 (2020) - IF : 3.756 (Q2)
• [Robissout et al. 2020] D. Robissout, G. Zaid, B. Colombier, L. Bossuet, A. Habrard, Online Performance Evaluation of Deep Learning Networks for Profiled Side-Channel Analysis, International Workshop on Constructive Side-Channel Analysis and Secure Design (COSADE), pp. 200-218, Lugano ( virtual ), Switzerland (2020)
• [Colombier et al. 2020] B. Colombier, D. Robissout, G. Zaid, L. Bossuet, A. Habrard, Apprentissage profond pour les attaques par analyse de canaux auxiliaires des implémentations de fonctions cryptographiques, Ecole d’hiver Francophone sur les Technologies de Conception des Systèmes embarqués Hétérogènes, FETCH, Montréal, Canada (2020)
• [Alazizi et al. 2020] A. Alazizi, A. Habrard, F. Jacquenet, L. He-Guelton, F. Oble, Dual Sequential Variational Autoencoders for Fraud Detection, 18th International Symposium on Intelligent Data Analysis (IDA), pp. 14-26, Konstanz, Germany (2020) - CORE Ranking : A
• [Germain et al. 2020] P. Germain, A. Habrard, F. Laviolette, E. Morvant, PAC-Bayes and Domain Adaptation, Neurocomputing, vol. 379(), pp. 379-397 (2020) - IF : 5.719 (Q1)
• [Kerdoncuff et al. 2020] T. Kerdoncuff, R. Emonet, M. Sebban, Metric Learning in Optimal Transport for Domain Adaptation, International Joint Conference on Artificial Intelligence, pp. 2162-2168, Kyoto, Japan (2020) - CORE Ranking : A*
• [Zaid et al. 2020b] G. Zaid, L. Bossuet, A. Habrard, A. Venelli, Methodology for Efficient CNN Architectures in Profiling Attacks, Workshop on Cryptographic Hardware and Embedded Systems, pp. 1-36, (2020) - CORE Ranking : A