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..
Provable convergence guarantees for black-box variational inference
Authors: Justin Domke, Robert Gower, Guillaume Garrigos
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We obtain non-asymptotic convergence guarantees for this problem, under simple assumptions. This provides rigorous guarantees that methods similar to those used in practice converge on realistic inference problems. |
| Researcher Affiliation | Collaboration | Justin Domke University of Massachusetts Amherst EMAIL, Guillaume Garrigos Université Paris Cité and Sorbonne Université, CNRS Laboratoire de Probabilités, Statistique et Modélisation F-75013 Paris, France EMAIL, Robert Gower Center for Computational Mathematics Flatiron Institute, New York EMAIL |
| Pseudocode | Yes | Algorithm 1 Prox-SGD with energy estimator and triangular factors, Algorithm 2 Proj-SGD with entropy estimator and symmetric factors, Algorithm 3 Proj-SGD with STL estimator and symmetric factors |
| Open Source Code | No | The paper does not contain any explicit statements about open-sourcing code or links to a code repository. |
| Open Datasets | No | This paper is theoretical and does not describe any experiments that would use datasets. |
| Dataset Splits | No | This paper is theoretical and does not describe any experiments that would specify data splits. |
| Hardware Specification | No | This paper is theoretical and does not describe any experimental setup or the hardware used. |
| Software Dependencies | No | This paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe any experimental setup or hyperparameters. |