OxCSML seminar this week: Laurence Midgley

[Hai Dang Dau]

Dear all,

This week we welcome Laurence Midgley from University of Cambridge to our OxCSML seminar. Please find details of the talk below.

Looking forward to seeing you there,

Saif & Hai-Dang

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Speaker: Laurence Midgley, University of Cambridge
Time and date: 14.00 - 15.00 Friday 17 Nov
Place: Room LG03 (Small Lecture Theatre), Dept of Statistics

Title: Flow Annealed Importance Sampling Bootstrap

Abstract: Normalizing flows are tractable density models that can approximate complicated target distributions, e.g. Boltzmann distributions of physical systems. However, current methods for training flows either suffer from mode-seeking behavior, use samples from the target generated beforehand by expensive MCMC methods, or use stochastic losses that have high variance. To avoid these problems, we augment flows with annealed importance sampling (AIS) and minimize the mass-covering alpha-divergence with alpha=2, which minimizes importance weight variance. Our method, Flow AIS Bootstrap (FAB), uses AIS to generate samples in regions where the flow is a poor approximation of the target, facilitating the discovery of new modes. We apply FAB to multimodal targets and show that we can approximate them very accurately where previous methods fail. To the best of our knowledge, we are the first to learn the Boltzmann distribution of the alanine dipeptide molecule using only the unnormalized target density, without access to samples generated via Molecular Dynamics (MD) simulations: FAB produces better results than training via maximum likelihood on MD samples while using 100 times fewer target evaluations. After reweighting the samples, we obtain unbiased histograms of dihedral angles that are almost identical to the ground truth.

Bio: Laurence is a PhD Student in Machine Learning and Chemistry at the University of Cambridge working with Prof. José Miguel Hernández-Lobato and his group. His focus is on generative models for the Boltzmann distribution of molecules.

Zoom: https://zoom.us/j/93402438267?pwd=ajd1a2g2bkx0LzNHRlh0ZXlIVnNFZz09