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..
Auxiliary Variational MCMC
Authors: Raza Habib, David Barber
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our sampler on a number of challenging distributions, where the underlying structure is known, and on the task of posterior sampling in Bayesian logistic regression. Code to reproduce all experiments is available at https://github.com/AVMCMC. ... 3 EXPERIMENTS |
| Researcher Affiliation | Academia | Raza. Habib Department of Computer Science University College London EMAIL David. Barber Department of Computer Science University College London EMAIL |
| Pseudocode | Yes | Algorithm 1: Auxiliary variational sampler |
| Open Source Code | Yes | Code to reproduce all experiments is available at https://github.com/AVMCMC. |
| Open Datasets | Yes | We use the heart data-set used by Song et al. (2017), which has 13 covariates and 270 data-points. |
| Dataset Splits | No | The paper mentions using the 'heart data-set used by Song et al. (2017)' but does not specify any explicit training, validation, or test dataset splits (e.g., percentages, counts, or methods for creating splits). |
| Hardware Specification | Yes | All methods were implemented using Tensorflow 1.10 (Abadi & Agarwal, 2015) and run on a single Tesla K80 GPU. |
| Software Dependencies | Yes | All methods were implemented using Tensorflow 1.10 (Abadi & Agarwal, 2015) |
| Experiment Setup | Yes | For HMC we use a number of initial runs to select an appropriate step-size and then tune the number of leapfrog steps. ... For the mixtures of Gaussians and ring density experiments we used an auxiliary dimension of 1 and the target dimension was 2. ... For Bayesian logistic regression, the target dimension was 14, the auxiliary dimension used was 2 dimensional and the number of hidden units was 300. The structure was otherwise the same as for the low dimensional experiments. |