Sample Adaptive MCMC
Authors: Michael Zhu
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the fast adaptation and effective sampling of SA-MCMC. |
| Researcher Affiliation | Academia | Michael H. Zhu Department of Computer Science Stanford University Stanford, CA 94305 mhzhu@cs.stanford.edu |
| Pseudocode | Yes | Algorithm 1 Sample Adaptive MCMC |
| Open Source Code | No | The paper does not include an unambiguous statement or direct link where the authors state they are releasing the code for the work described in this paper. |
| Open Datasets | Yes | classifying digits 7 vs. 9 on the MNIST dataset, and predicting whether an adult s income exceeds $50K/year based on the census income dataset from the UCI repository [52]. |
| Dataset Splits | Yes | For our experiment, we consider 10 regression parameters and a dataset of 10,000 points with a 80%/20% train/test split. ... The covtype dataset has a total of 581,012 data points, and we use a 80% training and 20% test split. |
| Hardware Specification | Yes | Our experiments and timing are done on a Intel Xeon E5-2640v3 using Julia v0.64 [51], except for NUTS which uses Stan C++. |
| Software Dependencies | Yes | Our experiments and timing are done on a Intel Xeon E5-2640v3 using Julia v0.64 [51], except for NUTS which uses Stan C++. ... RStan 2.19.2 [47] |
| Experiment Setup | Yes | The hyperparameters are (MH) q=.03; (MTM) q=.03, M=3; (AM) q=.03, s=.7; (SA) q0=1, N=40. |