@inproceedings{huggins2018random,
author = {Huggins, Jonathan and Mackey, Lester},
title = {Random Feature Stein Discrepancies},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
year = {2018},
month = {December},
abstract = {Computable Stein discrepancies have been deployed for a variety of applications, including sampler selection in posterior inference, approximate Bayesian inference, and goodness-of-fit testing. Existing convergence-determining Stein discrepancies admit strong theoretical guarantees but suffer from a computational cost that grows quadratically in the sample size. While linear-time Stein discrepancies have been proposed for goodness-of-fit testing, they exhibit avoidable degradations in testing power---even when power is explicitly optimized. To address these shortcomings, we introduce feature Stein discrepancies (ΦSDs), a new family of quality measures that can be cheaply approximated using importance sampling. We show how to construct ΦSDs that provably determine the convergence of a sample to its target and develop high-accuracy approximations---random ΦSDs (RΦSDs)---which are computable in near-linear time. In our experiments with sampler selection for approximate posterior inference and goodness-of-fit testing, RΦSDs typically perform as well or better than quadratic-time KSDs while being orders of magnitude faster to compute.},
url = {http://approjects.co.za/?big=en-us/research/publication/random-feature-stein-discrepancies/},
}