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Learning-Rate-Free Methods for Scalable Bayesian Inference

Tuesday, January 16, 2024 - 10:30 to 11:30
Louis Sharrock, Senior Research Associate in Statistical Machine Learning, Department of Mathematics and Statistics, Lancaster University
Statistics Seminar
ESB 4192 / Zoom

To join this seminar virtually: Please request Zoom connection details from ea [at]

Abstract: In recent years, particle-based variational inference (ParVI) methods such as Stein variational gradient descent 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 practitioners in order to ensure convergence to the target measure at a suitable rate. In this work, we introduce a suite of new particle-based methods for scalable Bayesian inference based on coin betting, which are entirely learning-rate free. We illustrate the performance of our approach on a range of numerical examples, including several high-dimensional models and datasets, demonstrating comparable or superior performance to other ParVI algorithms with no need to tune a learning rate.