Publications

Exhaustive consolidated list (automatic export)

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. 2024] 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), 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
• [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. 2024] 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. 2024] 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
• [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. 2024] 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)
• [Biłko et al. 2024] K. Biłko, 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)
• [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)

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)
• [Viallard et al. 2023] 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 (2023) - IF : 4.3 (Q2)