Auxiliary Variational MCMC
Authors: Raza Habib, David Barber
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | 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 raza.habib@cs.ucl.ac.uk David. Barber Department of Computer Science University College London david.barber@ucl.ac.uk |
| 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. |