Speaker |
Tom Rainforth |
|---|---|
Affiliation |
University of Oxford |
Date |
Friday, 24 March 2023 |
Time |
12:00-13:00 |
Location |
Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH |
Link |
https://ucl.zoom.us/j/97245943682 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
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. Related review paper: https://arxiv.org/abs/2302.14545 |
Biography |
I am a Senior Research Fellow in Machine Learning and a faculty member of the OxCSML Group in the Department of Statistics at the University of Oxford, where I run the RainML Research Lab (https://rainml.uk/). My 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 variational inference. |