Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions
Peng Chen · Keyi Wu · Joshua Chen · Tom O'Leary-Roseberry · Omar Ghattas

Wed Dec 11th 05:00 -- 07:00 PM @ East Exhibition Hall B + C #188

We propose a projected Stein variational Newton (pSVN) method for high-dimensional Bayesian inference. To address the curse of dimensionality, we exploit the intrinsic low-dimensional geometric structure of the posterior distribution in the high-dimensional parameter space via its Hessian (of the log posterior) operator and perform a parallel update of the parameter samples projected into a low-dimensional subspace by an SVN method. The subspace is adaptively constructed using the eigenvectors of the averaged Hessian at the current samples. We demonstrate fast convergence of the proposed method, complexity independent of the parameter and sample dimensions, and parallel scalability.

Author Information

Peng Chen (The University of Texas at Austin)
Keyi Wu (The University of Texas at Austin)
Joshua Chen (The University of Texas at Austin)
Tom O'Leary-Roseberry (The University of Texas at Austin)
Omar Ghattas (The University of Texas at Austin)