Stein Variational Gradient Descent With Matrix-Valued Kernels

Abstract

Stein variational gradient descent (SVGD) is a particle-based inference algorithm that leverages gradient information for efficient approximate inference. In this work, we enhance SVGD by leveraging preconditioning matrices, such as the Hessian and Fisher information matrix, to incorporate geometric information into SVGD updates. We achieve this by presenting a generalization of SVGD that replaces the scalar-valued kernels in vanilla SVGD with more general matrix-valued kernels. This yields a significant extension of SVGD, and more importantly, allows us to flexibly incorporate various preconditioning matrices to accelerate the exploration in the probability landscape. Empirical results show that our method outperforms vanilla SVGD and a variety of baseline approaches over a range of real-world Bayesian inference tasks.

Neural Information Processing Systems 2019

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• Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
• Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning
• Stein Variational Gradient Descent With Matrix-Valued Kernels
• Riemannian Stein Variational Gradient Descent for Bayesian Inference

Cited by in corpus (1)

• Stein Variational Gradient Descent With Matrix-Valued Kernels