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.

翻译：近年来，基于粒子的变分推断（ParVI）方法（如斯坦变分梯度下降（SVGD））作为可扩展的贝叶斯推断方法日益流行。然而，这类方法的性质通常依赖于学习率等超参数，实践者必须对其精心调节以确保以合适速率收敛到目标测度。本文基于投注策略（coin betting）引入一套全新的无学习率粒子方法，用于可扩展的贝叶斯推断。我们通过一系列数值示例（包括多个高维模型和数据集）展示了该方法的性能，结果表明其在不需调节学习率的情况下，与其他ParVI算法性能相当。