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..
Projected Stein Variational Gradient Descent
Authors: Peng Chen, Omar Ghattas
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present three Bayesian inference problems with high-dimensional parameters to demonstrate the accuracy of p SVGD compared to SVGD, and the convergence and scalability of p SVGD w.r.t. the number of parameters, samples, data points, and processor cores. |
| Researcher Affiliation | Academia | Peng Chen Omar Ghattas Oden Institute for Computational Engineering and Sciences The University of Texas at Austin Austin, TX 78712. EMAIL |
| Pseudocode | Yes | Algorithm 1 p SVGD in parallel |
| Open Source Code | Yes | In the numerical experiments for structured models, we use a backtracking line search method with Armijo Goldstein condition to look for the step size ϵl, where the line search objective function is taken as the negative log-posterior function, see more details in the accompanying code at https://github.com/cpempire/pSVGD. |
| Open Datasets | Yes | Bayesian logistic regression for binary classification of cancer and normal patterns for mass-spectrometric data with 10, 000 attributes from https://archive.ics.uci.edu/ml/datasets/Arcene |
| Dataset Splits | No | The paper states 'We use 100 data samples for training and 100 for testing.' but does not explicitly mention a separate validation split or its details. |
| Hardware Specification | Yes | The training time for p SVGD is 201 seconds compared to 477 seconds for SVGD in a Mac Book Pro Laptop (2019) with the processor of 2.4 GHz 8-Core Intel Core i9. |
| Software Dependencies | No | The paper mentions 'MPI' for parallelization and refers to 'accompanying code' but does not specify software dependencies with version numbers (e.g., Python, PyTorch, specific libraries). |
| Experiment Setup | Yes | We use the Euler-Maruyama scheme with step size t = 0.01 for the discretization, which leads to dimension d = 100 for the discrete Brownian path x. We run SVGD and the adaptive p SVGD with line search for the learning rate, using N = 128 samples... In Section 4.2: 'with 32 samples'. In Section 4.3: 'using 256 samples and 200 iterations for different dimensions'. |