Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev
Authors: Xiao Wang, Qi Lei, Ioannis Panageas
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work generalizes the results of Vempala and Wibisono [2019] where f is defined on a manifold M rather than Rn. From technical point of view, we show that KL decreases in a geometric rate whenever the distribution e f satisfies a log-Sobolev inequality on M. Our main technical contributions are: A non-asymptotic convergence guarantee for Geodesic Langevin algorithm on closed manifold is provided, with the help of log-Sobolev inequality. |
| Researcher Affiliation | Academia | Xiao Wang SUTD EMAIL Qi Lei Princeton EMAIL Ioannis Panageas UC Irvine EMAIL |
| Pseudocode | No | The paper describes the steps of the Geodesic Langevin Algorithm, but it does not present them in a formally structured pseudocode block or labeled 'Algorithm'. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that open-source code for the described methodology is provided. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus no information on publicly available training datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments that would involve dataset validation splits. |
| Hardware Specification | No | The paper does not mention any specific hardware used for experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, as it is a theoretical work. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameters or training configurations for empirical evaluation. |