Research map / Learning theory and algorithms / Sample complexity
On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions
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.
Closest papers by meaning
- Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence (NeurIPS 2024 · similarity 0.91)
- Decoupled Kullback-Leibler Divergence Loss (NeurIPS 2024 · similarity 0.84)
- Connecting Jensen–Shannon and Kullback–Leibler Divergences: A New Bound for Representation Learning (NeurIPS 2025 · similarity 0.84)
- Learning multivariate Gaussians with imperfect advice (ICML 2025 · similarity 0.84)
- Instance-Optimality for Private KL Distribution Estimation (NeurIPS 2025 · similarity 0.84)
- Better Estimation of the Kullback--Leibler Divergence Between Language Models (NeurIPS 2025 · similarity 0.84)
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.
