Catalyzing Conversation: The Royal Statistical Society’s Webinar on Dalalyan’s Paper ‘Theoretical Guarantees for Approximate Sampling from Smooth and Log-Concave Densities'”

On 31 October, the Royal Statistical Society webinar was devoted to Arnak S. Dalalyan’s 2017 Series B paper ‘Theoretical Guarantees for Approximate Sampling from Smooth and Log-Concave Densities’, featuring contributions from Hani Doss and Alain Durmus.

“[Dalalyan] combines techniques from convex optimisation with insights from random processes to provide non-asymptotic guarantees regarding the accuracy of sampling from a target probability density. These guarantees are notably simpler than those found in the existing literature, and they remain unaffected by dimensionality.

The findings pave the way for more widespread adoption of the mathematical and algorithmic tools developed in the field of convex optimization within the domains of statistics and machine learning.”

Showcasing significant recent papers published in the Society’s journals, the journal webinar format aims to bring authors closer to their audience in academia and industry. Impactful features of the paper are presented by the author, followed by contributions from the guest discussants.