Speaker |
Eugenio Clerico |
|---|---|
Affiliation |
University of Oxford |
Date |
Friday, 18 November 2022 |
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 |
We establish a disintegrated PAC-Bayesian bound for classifiers that are trained via continuous-time (non-stochastic) gradient descent. Contrarily to what is standard in the PAC-Bayesian setting, our result applies to a training algorithm that is deterministic, conditioned on a random initialisation, without requiring any de-randomisation step. We provide a broad discussion of the main features of the bound that we propose, and we study analytically and empirically its behaviour on linear models, finding promising results. |
Biography |
Eugenio Clerico is a final year DPhil student, supervised by Arnaud Doucet and George Deligiannidis. Before arriving in Oxford, he obtained a Bachelor’s degree in Physics at the University of Pavia (Italy) and a Master’s degree in theoretical Physics at the École Normale Supérieure in Paris. His current research lies in statistical learning theory and its applications to modern deep learning algorithms. More precisely, he has been working mostly on generalisation bounds in the PAC-Bayesian and information-theoretic frameworks, and on the Gaussian behaviour of neural networks in the limit of infinite width. |