Personal profile
Personal profile
Florence d’Alché-Buc has been since 2014 a full professor at Télécom Paris, an IMT grande école. Previously, she was a professor at Université d’Evry, an ATIGE (Genopole Thematic Incentive Actions) researcher and joint head of the IBISC lab. She launched and managed the Challenges program as part of the PASCAL European network (2004-08) and since 2017, has become the scientific director of the Digiscome Labex. Her research is on machine learning, network inference, structured prediction and dynamical system modeling.
Since January 2019, she holds the Data Science and Artificial Intelligence for Digitalized Industry and Services Research and Teaching Chair.
From September 2021, she is Images, Data, Signal department head.
She has authored more than 80 articles in international journals and conference proceedings.
Research Interests
Machine Learning & Artificial Intelligence, bioinformatics & medical applications.
Subdomains: (Operator-valued) Kernel Methods, Structured Output Prediction, Reliable Machine Learning, Dynamical Systems Modeling.
Teaching
As part of her teaching work, she was for several years co-head of the Data Science Master 2 at Université Paris-Saclay jointly awarded by École Polytechnique, ENSAE Paris and Université Paris Sud. She participated in the conception of the new continuous education programs in artificial intelligence: a Post-Master Degree and a specialist certificate. She is also responsible for the Bearing Point Data Science Education Chair.
@Télécom Paris
- Machine Learning (2nD Year, level Master 1)
- PACT Project
- PRIM Project
@Ecole polytechnique
Master of Mathematics and applications, Université Paris-Saclay
- Advanced Machine Learning: from theory to practise
- Structured Data: learning, prediction, dependency, testing
@Télécom Evolution
Professional training:
- Specialized Certificates Studies (CES) Data Scientist: Introduction to Machine Learning
- Machine Learning and Advanced Machine Learning
Research interests
Education/Academic qualification
HDR (PhD Supervision Credentials)
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Collaborations and top research areas from the last five years
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RESTYLING UNSUPERVISED CONCEPT BASED INTERPRETABLE NETWORKS WITH GENERATIVE MODELS
Parekh, J., Bouniot, Q., Mozharovskyi, P., Newson, A. & d'Alché-Buc, F., 1 Jan 2025, 13th International Conference on Learning Representations, ICLR 2025. International Conference on Learning Representations, ICLR, p. 25211-25245 35 p. (13th International Conference on Learning Representations, ICLR 2025).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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TAILORING MIXUP TO DATA FOR CALIBRATION
Bouniot, Q., Mozharovskyi, P. & d'Alché-Buc, F., 1 Jan 2025, 13th International Conference on Learning Representations, ICLR 2025. International Conference on Learning Representations, ICLR, p. 6766-6796 31 p. (13th International Conference on Learning Representations, ICLR 2025).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
Krzakala, P., Yang, J., Flamary, R., d'Alché-Buc, F., Laclau, C. & Labeau, M., 1 Jan 2024, In: Advances in Neural Information Processing Systems. 37Research output: Contribution to journal › Conference article › peer-review
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A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions
Staerman, G., Mozharovskyi, P., Colombo, P., Clémençon, S. & D’alché-Buc, F., 1 Jan 2024, In: Transactions on Machine Learning Research. 2024Research output: Contribution to journal › Article › peer-review
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Exploiting Edge Features in Graph-based Learning with Fused Network Gromov-Wasserstein Distance
Yang, J., Labeau, M. & D’alché-Buc, F., 1 Jan 2024, In: Transactions on Machine Learning Research. 2024Research output: Contribution to journal › Article › peer-review
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Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
El Ahmad, T., Brogat-Motte, L., Laforgue, P. & d’Alché-Buc, F., 1 Jan 2024, In: Proceedings of Machine Learning Research. 238, p. 109-117 9 p.Research output: Contribution to journal › Conference article › peer-review
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Tackling Interpretability in Audio Classification Networks With Non-negative Matrix Factorization
Parekh, J., Parekh, S., Mozharovskyi, P., Richard, G. & D'alche-Buc, F., 1 Jan 2024, In: IEEE/ACM Transactions on Audio Speech and Language Processing. 32, p. 1392-1405 14 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Fast Kernel Methods for Generic Lipschitz Losses via p-Sparsified Sketches
Ahmad, T. E., Laforgue, P. & D’alché-Buc, F., 1 Sept 2023, In: Transactions on Machine Learning Research. 2023Research output: Contribution to journal › Article › peer-review
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Wind power predictions from nowcasts to 4-hour forecasts: A learning approach with variable selection
Bouche, D., Flamary, R., d'Alché-Buc, F., Plougonven, R., Clausel, M., Badosa, J. & Drobinski, P., 1 Jul 2023, In: Renewable Energy. 211, p. 938-947 10 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Interpretable Generative Modeling Using a Hierarchical Topological VAE
Desticourt, E., Letort, V. & D'Alche-Buc, F., 1 Jan 2022, Proceedings - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022. Institute of Electrical and Electronics Engineers Inc., p. 1415-1421 7 p. (Proceedings - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review