Michele Caprio

Michele Caprio

Dr

Accepting PhD Students

PhD projects

Multimodal Credal Learning Theory (CDT Project, joint with Tingting Mu)

Personal profile

Biography

Michele is a Lecturer (Assistant Professor) in Machine Learning and Artificial Intelligence at The University of Manchester. He obtained his PhD in Statistics from Duke University, and worked as a postdoctoral researcher in the Department of Computer and Information Science of the University of Pennsylvania. His general interest is probabilistic machine learning, and in particular the use of imprecise probabilistic techniques to investigate the theory and methodology of uncertainty quantification in Machine Learning and Artificial Intelligence. Recently, he won the IJAR Young Researcher and the IMS New Researcher Awards, and he was elected member of the London Mathematical Society.

Education/Academic qualification

Doctor of Philosophy, Advances in Choquet Theories, Duke University

Award Date: 8 May 2022

Areas of expertise

  • QA75 Electronic computers. Computer science
  • Imprecise Probabilistic Machine Learning
  • Uncertainty Quantification
  • Applied Probability

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  • Credal Bayesian Deep Learning

    Caprio, M., Dutta, S., Jang, K. J., Lin, V., Ivanov, R., Sokolsky, O. & Lee, I., Oct 2024, In: Transactions on Machine Learning Research .

    Research output: Contribution to journalArticlepeer-review

    Open Access
    File
    15 Downloads (Pure)
  • Credal Learning Theory

    Caprio, M., Sultana, M., Elia, E. G. & Cuzzolin, F., Oct 2024, p. 1-31. 31 p.

    Research output: Contribution to conferencePaperpeer-review

    File
    27 Downloads (Pure)
  • DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning Models

    Lin, V., Jang, K. J., Dutta, S., Caprio, M., Sokolsky, O. & Lee, I., 15 Jul 2024, Proceedings of Machine Learning Research: 6th Annual Learning for Dynamics & Control Conference, 15-17 July 2024, University of Oxford, Oxford, UK. Vol. 242. p. 1526-1538

    Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

    Open Access
    File
    32 Downloads (Pure)
  • Second-Order Uncertainty Quantification: A Distance-Based Approach

    Sale, Y., Bengs, V., Caprio, M. & Hüllermeier, E., 1 Jul 2024, (Accepted/In press) Proceedings of machine Learning Research, ICML 2024.

    Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

    153 Downloads (Pure)
  • A Novel Bayes' Theorem for Upper Probabilities

    Caprio, M., Sale, Y., Hüllermeier, E. & Lee, I., 2023, Proceedings of Machine Learning Research, Epi-UAI 2023.

    Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

    File
    32 Downloads (Pure)