Imprecise Probabilistic Machine Learning - Being Precise about Imprecision by Dr. Michele Caprio
| Dates: | 5 March 2025 |
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| Times: | 13:00 - 14:00 |
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| What is it: | Seminar |
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| Organiser: | Department of Computer Science |
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| How much: | Free |
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| Who is it for: | University staff, Current University students |
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| Speaker: | Dr. Michele Caprio |
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In this talk, I will introduce myself to my new colleagues. After that, I will (briefly) talk about the history of Imprecise Probabilities (IPs) — my main research area — from their inception in Philosophy, to their later adoption in Statistics and other sciences. I'll make the case for why IPs are useful and indeed needed in (Probabilistic) Machine Learning methodology and theory. If time allows it, I will conclude with a recent result in Imprecise Probabilistic Machine Learning theory concerning the ergodic behaviour of Imprecise Markov Semigroups. Such a result allows us to study the long-term behaviour of smooth input transitions for Convolutional Autoencoders, in the presence of uncertainty and ambiguity.
Speaker
Dr. Michele Caprio
Role: Lecturer (Assistant Professor) in Machine Learning
Organisation: University of Manchester
Biography: Michele is a Lecturer (Assistant Professor) in Machine Learning 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.
Travel and Contact Information
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Kilburn Building
Manchester
