  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 paper, 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 performance to other ParVI
algorithms with no need to tune a learning rate.
