AI Paper Atlas

Research map / Probabilistic and scientific modeling / Variational and generative sampling

Approximate Stein Classes for Truncated Density Estimation

ICML 2023 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Likelihood-free and graphical model estimation · 0 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
Replace exact boundary-distance weighting with approximate Stein class built from boundary samples (TKSD)truncated density estimation · US-border truncated Gaussianmean l2 estimation errorHelped
Approximate boundary-distance function used by TruncSM (instead of exact) on l1 and l2 truncation (ablation)TruncSM / bd-KSD · Gaussian truncated by l1/l2 ballsmean l2 estimation errorHurt
Use TKSD instead of exact-distance TruncSM/bd-KSD on l2 ball truncationtruncated Gaussian, l2 ball · Gaussian truncated by l2 ballmean l2 estimation errorHelped
Use TKSD on l1 ball truncation versus TruncSM/bd-KSDtruncated Gaussian, l1 ball · Gaussian truncated by l1 ballmean l2 estimation errorNo clear effect
Use TKSD instead of exact-distance methods for runtimetruncated Gaussian, l1 ball · Gaussian truncated by l1 ballruntimeHelped

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