Speaker |
Alberto Caron |
|---|---|
Affiliation |
Alan Turing Institute |
Date |
Friday, 24 November 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 |
Structure learning is of interests in many disciplines (e.g., genomics, biology, ecology, etc.) where the aim is to reconstruct a graphical model, in the form of a Directed Acyclic Graph (DAG), underlying a set of random variables. Bayesian methods have demonstrated superiority, particularly in low data regimes, for their ability to learn a distribution over possible DAGs rather than just a Maximum A Posteriori. After briefly introducing the problem of (Bayesian) structure learning, and reviewing some of the popular MCMC based approaches, we propose a novel sampler, PARNI-DAG, that performs efficient sampling from the posterior on DAGs via a locally informed, adaptive random neighbourhood proposal that results in better mixing properties. We demonstrate PARNI-DAG mixing properties and accuracy in DAG learning on a series of experimental setups. |
Biography |
Alberto is a Research Associate at The Alan Turing Institute, affiliated with the AI for Cyber-Defence team, where he currently works on projects involving causality and sequential decision making under uncertainty. Prior to that, he completed his PhD studies on Bayesian Causal Inference at the UCL Department of Statistical Science, under the supervision of Prof. Ioanna Manolopoulou and Prof. Gianluca Baio. |