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.

翻译：近年来，基于粒子的变分推断方法（如斯坦因变分梯度下降法）作为可扩展的贝叶斯推断工具日益受到关注。然而，这类方法的性能始终依赖于学习率等超参数——实践者必须精心调整这些参数以确保以适当速率收敛至目标测度。本文基于"硬币投注"策略提出了一套全新无学习率的粒子方法，实现了完全免调参的可扩展贝叶斯推断。通过涵盖多个高维模型与数据集的数值实验，我们展示了该方法在性能上可与现有粒子变分推断算法相媲美的表现。