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..
Understanding Stochastic Natural Gradient Variational Inference
Authors: Kaiwen Wu, Jacob R. Gardner
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section presents supporting numerical simulations on datasets from the UCI repository (Bike and Mushroom) and MNIST (Kelly et al., 2017; Le Cun et al., 1998) [...] Figure 1 presents Bayesian linear regression on the Bike dataset (n = 17, 389)... Figure 2 shows Bayesian logistic regression on the Mushroom dataset (n = 8124) and MNIST... |
| Researcher Affiliation | Academia | Kaiwen Wu 1 Jacob R. Gardner 1 1Department of Computer and Information Science, University of Pennsylvania, Philadelphia, United States. |
| Pseudocode | Yes | Algorithm 1: Auto Differentiation Stochastic Gradient |
| Open Source Code | No | The paper does not provide any explicit statements or links about releasing open-source code for the methodology described. |
| Open Datasets | Yes | datasets from the UCI repository (Bike and Mushroom) and MNIST (Kelly et al., 2017; Le Cun et al., 1998) |
| Dataset Splits | No | The paper does not specify explicit training/validation/test dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'torch' and 'torch.distributions' in Algorithm 1, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | SGD uses a step size schedule γt = 1 / (10^5+t)... Stochastic NGD uses a step size schedule γt = 2 / (2+t)... The (negative) ELBO is optimized by SGD and stochastic NGD with a mini-batch size of 1000. and On Mushroom, the step size of SGD is tuned from {10^-3, 10^-4, 10^-5, 10^-6}, while the step size of NGD is tuned from {5*10^-1, 10^-1, 10^-2, 10^-3}. ... We use 10 samples from the variational distribution to estimate the stochastic gradient in every iteration. |