Flexible and efficient inference with particles for the variational Gaussian approximation

Variational inference is a powerful framework, used to approximate intractable posteriors through variational distributions. The de facto standard is to rely on Gaussian variational families, which come with numerous advantages: they are easy to sample from, simple to parametrize, and many expectations are known in closed-form or readily computed by quadrature. In this paper, we view the Gaussian variational approximation problem through the lens of gradient flows. We introduce a flexible and efficient algorithm based on a linear flow leading to a particle-based approximation. We prove that, with a sufficient number of particles, our algorithm converges linearly to the exact solution for Gaussian targets, and a low-rank approximation otherwise. In addition to the theoretical analysis, we show, on a set of synthetic and real-world high-dimensional problems, that our algorithm outperforms existing methods with Gaussian targets while performing on a par with non-Gaussian targets.DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berli

JuliaGaussianProcesses/AugmentedGPLikelihoods.jl: v0.4.17

<h2>AugmentedGPLikelihoods v0.4.17</h2> <p><a href="https://github.com/JuliaGaussianProcesses/AugmentedGPLikelihoods.jl/compare/v0.4.16...v0.4.17">Diff since v0.4.16</a></p> <p><strong>Merged pull requests:</strong></p> <ul> <li>Stable version of logpdf (#49) (@theogf)</li> <li>CompatHelper: add new compat entry for IrrationalConstants at version 0.1 for package test, (keep existing compat) (#116) (@github-actions[bot])</li> <li>Update versions (#122) (@theogf)</li> </ul&gt

TuringLang/EllipticalSliceSampling.jl: v2.0.0

<h2>EllipticalSliceSampling v2.0.0</h2> <p><a href="https://github.com/TuringLang/EllipticalSliceSampling.jl/compare/v1.1.0...v2.0.0">Diff since v1.1.0</a></p> <p><strong>Merged pull requests:</strong></p> <ul> <li>Rename <code>init_params</code> keyword argument to <code>initial_params</code> (#33) (@devmotion)</li> </ul&gt

JuliaGaussianProcesses/KernelFunctions.jl: v0.10.58

<h2>KernelFunctions v0.10.58</h2> <p><a href="https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/compare/v0.10.57...v0.10.58">Diff since v0.10.57</a></p> <p><strong>Merged pull requests:</strong></p> <ul> <li>Fix Matern AD failures (#528) (@simsurace)</li> <li>Stop using deprecated signatures from Distances.jl (#529) (@simsurace)</li> </ul&gt

TuringLang/AdvancedHMC.jl: v0.6.0

<h2>AdvancedHMC v0.6.0</h2> <p><a href="https://github.com/TuringLang/AdvancedHMC.jl/compare/v0.5.5...v0.6.0">Diff since v0.5.5</a></p> <p><strong>Merged pull requests:</strong></p> <ul> <li>fix: arg order (#349) (@xukai92)</li> <li>CompatHelper: bump compat for AbstractMCMC to 5, (keep existing compat) (#352) (@github-actions[bot])</li> <li>Deprecate <code>init_params</code> which is no longer in AbstractMCMC (#353) (@torfjelde)</li> <li>CompatHelper: add new compat entry for Statistics at version 1, (keep existing compat) (#354) (@github-actions[bot])</li> <li>Removed deprecation of init_params + bump minor version (#355) (@torfjelde)</li> <li>Fix some tests. (#356) (@yebai)</li> <li>Fix docs CI (#357) (@yebai)</li> </ul> <p><strong>Closed issues:</strong></p> <ul> <li>Doc string error for NUTS (#346)</li> </ul&gt