atlas

Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes

Sparse Variational Inference: Bayesian Coresets from Scratch

NeurIPS 2019 · Probabilistic and scientific modeling › Uncertainty and Gaussian processes · approach: Variational Bayesian deep learning · 4 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use SparseVI instead of fixed-reference Hilbert coreset constructionBayesian coresets · Synthetic Gaussian posterior inferenceKL divergence to the exact posteriorHelped
Use SparseVI instead of GIGA with an optimal weighting distributionBayesian coresets · UK housing-price radial basis function regressionKL divergence to the exact posteriorMixed
Use GIGA with a realistic weighting distribution instead of uniform random subsamplingBayesian coresets · UK housing-price radial basis function regressionKL divergence to the exact posteriorHurt
Use SparseVI instead of GIGABayesian coresets · Logistic and Poisson regression datasetsKL divergence to the exact posteriorHelped
Use SparseVI instead of GIGABayesian coresets · Logistic and Poisson regression datasetscomputation timeHurt
Use SparseVI instead of practical coreset construction methodsBayesian coresets · Logistic and Poisson regression datasetsKL divergence to the exact posteriorHelped

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy