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 [1].
Variational Inference with Gaussian Score Matching
Authors: Chirag Modi, Robert Gower, Charles Margossian, Yuling Yao, David Blei, Lawrence Saul
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically compared GSM-VI to reparameterization BBVI on several classes of models, and with both synthetic and real-world data. We evaluate how well GSM-VI can approximate a variety of target posterior distributions. |
| Researcher Affiliation | Collaboration | Chirag Modi Center for Computational Astrophysics, Center for Computational Mathematics, Flatiron Institute, New York EMAIL Charles C. Margossian Center for Computational Mathematics, Flatiron Institute, New York EMAIL Yuling Yao Center for Computational Mathematics, Flatiron Institute, New York EMAIL Robert M. Gower Center for Computational Mathematics, Flatiron Institute, New York EMAIL David M. Blei Department of Computer Science, Statistics, Columbia University, New York EMAIL Lawrence K. Saul Center for Computational Mathematics, Flatiron Institute, New York EMAIL |
| Pseudocode | Yes | Algorithm 1: Gaussian Score Matching VI; Algorithm 2: Black-box variational inference |
| Open Source Code | Yes | We provide a Python implementation of GSM-VI algorithm at https://github.com/modichirag/GSM-VI. |
| Open Datasets | Yes | a collection of real-world Bayesian inference problems from the posterior DB database of datasets and models. M. Magnusson, P. Bürkner, and A. Vehtari. posteriordb: a set of posteriors for Bayesian inference and probabilistic programming, November 2022. URL https://github.com/ stan-dev/posteriordb. |
| Dataset Splits | No | The paper uses synthetic models where the true distribution is known, and for real-world models, it refers to the posteriordb. However, it does not provide explicit details on training, validation, and test dataset splits with percentages or counts for reproducibility. |
| Hardware Specification | No | The paper mentions running times but does not specify any hardware details like GPU/CPU models or specific machine configurations used for experiments. |
| Software Dependencies | No | The paper mentions 'Python implementation' and 'Jax', 'ADAM optimizer [19]', and 'bridgestan [6, 30]' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The only free parameter in GSM-VI is the batch size B. ... In all studies of this section we report results for B = 2 and show that it is a good default baseline. We use the same batch size for BBVI. ... Unless specified otherwise, we initialize the variational approximation as a Gaussian distribution with zero mean and identity covariance matrix. ... We use the ADAM optimizer [19] with default settings but vary the learning rate between 10⁻¹ and 10⁻³. |