Parallel Tempering With a Variational Reference
Authors: Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté, Trevor Campbell
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The paper concludes with experiments that demonstrate the large empirical gains achieved by our method in a wide range of realistic Bayesian inference scenarios. |
| Researcher Affiliation | Academia | Nikola Surjanovic Department of Statistics University of British Columbia nikola.surjanovic@stat.ubc.ca Saifuddin Syed Department of Statistics University of Oxford saifuddin.syed@stats.ox.ac.uk Alexandre Bouchard-Côté Department of Statistics University of British Columbia bouchard@stat.ubc.ca Trevor Campbell Department of Statistics University of British Columbia trevor@stat.ubc.ca |
| Pseudocode | Yes | Algorithm 1 Non-reversible parallel tempering (NRPT) |
| Open Source Code | Yes | The code for the experiments is made publicly available: Julia code is available at https://github.com/UBC-Stat-ML/Variational PT and Blang code is at https://github.com/UBC-Stat-ML/bl-vpt-nextflow. |
| Open Datasets | Yes | We consider various Bayesian inference problems: 11 based on real data, and 4 based on synthetic data (see Table 1 in Appendix F for the details of each). |
| Dataset Splits | No | The paper does not provide specific details on training/validation/test splits of the datasets, only general usage for experiments. |
| Hardware Specification | No | We also acknowledge use of the ARC Sockeye computing platform from the University of British Columbia. This specifies a platform but lacks specific hardware details like GPU/CPU models. |
| Software Dependencies | No | The paper mentions 'Julia code' and 'Blang code' but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | No | Experimental details can be found in Appendix F. The main body of the paper mentions that methods have 'comparable cost per iteration' and 'same total number of chains and iterations' but does not provide specific hyperparameters or system-level training settings. |