OxCSML seminar this week: Louis Sharrock on Coin Sampling

[Hai Dang Dau]

Dear all,

Please find below the details for the OxCSML seminar this week. We welcome Louis Sharrock from Lancaster University to present his work on Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates. Looking forward to seeing you there.

Best,

Hai-Dang

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Speaker: Louis Sharrock (Lancaster University)

Time and date: 14:00 - 15:00, Friday 9 June 2023.

Place: Department of Statistics, University of Oxford. Room LG03 (Small Lecture Theatre).

Zoom link registration: https://www.eventbrite.co.uk/e/oxcsml-seminar-9-june-2023-louis-sharrock-tickets-648033655107

Title: Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates

Abstract: In recent years, particle-based variational inference (ParVI) methods such as Stein variational gradient descent (SVGD) have grown in popularity as scalable methods for Bayesian inference. Unfortunately, the properties of such methods invariably depend on hyperparameters such as the learning rate, which must be carefully tuned by the practitioner in order to ensure convergence to the target measure at a suitable rate. In this talk, we will discuss a suite of new particle-based methods for scalable Bayesian inference based on coin betting, which are entirely learning-rate free. By leveraging the viewpoint of sampling as an optimisation problem on the space of probability measures, we will outline how to establish convergence of these methods to the target measure in the log-concave setting. We will then illustrate the performance of these methods on a number of examples, including several high-dimensional models and datasets, demonstrating comparable performance to existing ParVI algorithms with no need to tune a learning rate.