Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
Authors: Yizhe Zhang, Xiangyu Wang, Changyou Chen, Ricardo Henao, Kai Fan, Lawrence Carin
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results are validated with synthetic data and real-world applications. and In the experiments, we validate our theory on both synthetic data and with real-world problems, including Bayesian Logistic Regression (BLR) and Independent Component Analysis (ICA), for which we compare the mixing performance of our approach with that of standard HMC and slice sampling. |
| Researcher Affiliation | Academia | Duke University Durham, NC, 27708 {yz196,xw56,changyou.chen, ricardo.henao, kf96 , lcarin} @duke.edu |
| Pseudocode | Yes | Algorithm 1: MG-HMC with HJE and Algorithm 2: MG-SS |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code for the described methodology, nor does it include any links to code repositories. |
| Open Datasets | Yes | We evaluate our methods on 6 real-world datasets from the UCI repository [20]: German credit (G), Australian credit (A), Pima Indian (P), Heart (H), Ripley (R) and Caravan (C) [21]. |
| Dataset Splits | No | The paper mentions 'Prediction accuracies estimated via cross-validation' but does not provide specific details on the train/validation/test dataset splits, such as percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or memory specifications used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as library names and their corresponding versions. |
| Experiment Setup | Yes | Each method is run for 30,000 iterations with 10,000 burn-in samples. The number of leap-frog steps is set to be uniformly drawn from (100 l, 100 + l) with l = 20, as suggested by [16]. and Other experimental settings (m and ) are provided in the Appendix. |