Personal profile
Personal profile
Jesse Read is a Professor (HDR) in the DaSciM team of the Data Analytics and Machine Learning pôle of the Computer Science Laboratory (LIX) at École Polytechnique, Institut Polytechnique de Paris.
His main research interests involve multi-target learning, probabilistic inference, and learning from dynamic and evolving data streams, but he is open to much more, including applications in medicine and energy.
Prior to becoming Professor (HDR 2017), he was Assistant Professor (also at LIX, Ecole Polytechnique), following postdoc research at INFRES Télécom Paris (2016), Aalto University, Finland (2013-2016), and the Dept. TSC at the Universidad Carlos III de Madrid, Spain (2010-2013). He obtained his PhD in 2010 from the University of Waikato, New Zealand. He obtained a Bachelor of Computing and Mathematical Sciences (Hons.) also at Waikato.
Research interests
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Collaborations and top research areas from the last five years
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I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation
Thil, L., Read, J., Kaddah, R. & Doquet, G., 1 Jan 2026, Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2025, Proceedings. Ribeiro, R. P., Soares, C., Gama, J., Pfahringer, B., Japkowicz, N., Larrañaga, P., Jorge, A. M. & Abreu, P. H. (eds.). Springer Science and Business Media Deutschland GmbH, p. 395-411 17 p. (Lecture Notes in Computer Science; vol. 16018 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Open Access -
A Survey of Monte Carlo Methods for Noisy and Costly Densities With Application to Reinforcement Learning and ABC
Llorente, F., Martino, L., Read, J. & Delgado-Gómez, D., 1 Apr 2025, In: International Statistical Review. 93, 1, p. 18-61 44 p.Research output: Contribution to journal › Article › peer-review
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Branches: Efficiently Seeking Optimal Sparse Decision Trees via AO*
Chaouki, A., Read, J. & Bifet, A., 1 Jan 2025, In: Proceedings of Machine Learning Research. 267, p. 7430-7484 55 p.Research output: Contribution to journal › Conference article › peer-review
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Determining the Need for Multi-label Classifiers by Measuring Unexplained Covariance
Park, L. A. F. & Read, J., 1 Jan 2025, Data Science: Foundations and Applications - 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025, Proceedings. Wu, X., Spiliopoulou, M., Wang, C., Kumar, V., Cao, L., Zhou, X., Pang, G. & Gama, J. (eds.). Springer Science and Business Media Deutschland GmbH, p. 250-261 12 p. (Lecture Notes in Computer Science; vol. 15875 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Estimating Multi-Label Expected Accuracy Using Labelset Distributions
Park, L. A. F. & Read, J., 1 Jan 2025, In: IEEE Transactions on Knowledge and Data Engineering. 37, 5, p. 2513-2524 12 p.Research output: Contribution to journal › Article › peer-review
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Estimating Multi-Label Expected Accuracy Using Labelset Distributions
Park, L. A. F. & Read, J., 1 Jan 2025, In: IEEE Transactions on Knowledge and Data Engineering. 37, 5, p. 2513-2524 12 p.Research output: Contribution to journal › Article › peer-review
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Supervised Learning from Data Streams: An Overview and Update
Read, J. & Zliobaite, I., 11 Jul 2025, In: ACM Computing Surveys. 57, 12, ART307.Research output: Contribution to journal › Article › peer-review
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A Survey of Monte Carlo Methods for Noisy and Costly Densities With Application to Reinforcement Learning and ABC
Llorente, F., Martino, L., Read, J. & Delgado-Gómez, D., 1 Jan 2024, (Accepted/In press) In: International Statistical Review.Research output: Contribution to journal › Article › peer-review
Open Access -
Autoreplicative random forests with applications to missing value imputation
Antonenko, E., Carreño, A. & Read, J., 1 Oct 2024, In: Machine Learning. 113, 10, p. 7617-7643 27 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Backward Inference in Probabilistic Regressor Chains with Distributional Constraints
Antonenko, E., Mechenich, M., Beigaitė, R., Žliobaitė, I. & Read, J., 1 Jan 2024, Advances in Intelligent Data Analysis XXII - 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Proceedings. Miliou, I., Piatkowski, N. & Papapetrou, P. (eds.). Springer Science and Business Media Deutschland GmbH, p. 43-55 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14642 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review