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..
Stochastic Gradient Monomial Gamma Sampler
Authors: Yizhe Zhang, Changyou Chen, Zhe Gan, Ricardo Henao, Lawrence Carin
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | It is shown that the proposed approach is better at exploring complex multimodal posterior distributions, as demonstrated on multiple applications and in comparison with other stochastic gradient MCMC methods. |
| Researcher Affiliation | Academia | 1Duke University, Durham, NC, 27708. Correspondence to: Yizhe Zhang <EMAIL>. |
| Pseudocode | Yes | The complete update scheme, with Euler integrator, for SGMGT is presented in the SM. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We evaluated the mixing ef๏ฌciency and accuracy of SGMGT and SGMGT-D using Bayesian logistic regression (BLR) on 6 real-world datasets from the UCI repository (Bache & Lichman, 2013): German credit (G), Australian credit (A), Pima Indian (P), Heart (H), Ripley (R) and Caravan (C). |
| Dataset Splits | Yes | We use 80% of the documents for training and the remaining 20% for testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions models and algorithms but does not specify version numbers for any software dependencies or libraries used for implementation (e.g., PyTorch, TensorFlow, scikit-learn versions). |
| Experiment Setup | Yes | We set the minibatch size to 16. Other hyperparameters are provided in the SM. For each experiment, we draw 5000 iterations with 1000 burn-in samples. |