Tom Rainforth - Modern Bayesian Experimental Design | ML in PL 2024
Автор: ML in PL
Загружено: 2025-03-03
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Bayesian experimental design (BED) provides a powerful and general framework for optimizing the design of experiments. However, its deployment often poses substantial computational challenges that can undermine its practical use. In this talk, I will outline how recent advances have transformed our ability to overcome these challenges and thus utilize BED effectively, before discussing some key areas for future development in the field.
Tom is a Senior Researcher in Machine Learning and leader of the RainML Research Lab at the Department of Statistics in the University of Oxford. He is the principal investigator for the ERC Starting Grant Data-Driven Algorithms for Data Acquisition. His research covers a wide range of topics in and around machine learning and experimental design, with areas of particular interest including Bayesian experimental design, deep learning, representation learning, generative models, Monte Carlo methods, active learning, probabilistic programming, and approximate inference.
This talk was one of the Invited Talks at the ML in PL Conference 2024.
ML in PL Conference 2024 website: https://conference2024.mlinpl.org
ML in PL Association Website: https://mlinpl.org
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