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..
Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables
Authors: Guangyao Zhou
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The superior performances of M-HMC over existing methods are demonstrated with numerical experiments on Gaussian mixture models (GMMs), variable selection in Bayesian logistic regression (BLR), and correlated topic models (CTMs). |
| Researcher Affiliation | Industry | Guangyao Zhou Vicarious AI Union City, CA 94587, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 M-HMC with Laplace momentum |
| Open Source Code | Yes | Code available at https://github.com/Stannis Zhou/mixed_hmc |
| Open Datasets | Yes | We use the Associated Press (AP) dataset [15], which consists of 2246 documents. |
| Dataset Splits | No | The paper specifies burn-in and actual sample counts for MCMC chains but does not provide explicit training, validation, or test dataset splits in the traditional machine learning sense for model training. |
| Hardware Specification | No | The paper mentions that implementations rely on JAX but does not specify any particular CPU or GPU models, or other hardware details used for running experiments. |
| Software Dependencies | No | The paper mentions software like JAX, NUMBA, pypolyagamma, Numpyro, and arviz, but it does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | For each sampler, we draw 104 burn-in and 104 actual samples in 192 independent chains. For each sampler, we use 192 independent chains, each with 1000 burn-in and 2000 actual samples. For M-HMC, we inspect short trial runs on a separate document, and fix T, n D for all 20 picked documents and set L = 80 Nd for document d. |