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..
Optimal Underdamped Langevin MCMC Method
Authors: Zhengmian Hu, Feihu Huang, Heng Huang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both synthetic and real-world data show that our new method consistently outperforms the existing ULD approaches. |
| Researcher Affiliation | Academia | Zhengmian Hu, Feihu Huang, Heng Huang Department of Electrical and Computer Engineering University of Pittsburgh, Pittsburgh, PA 15213, USA |
| Pseudocode | Yes | Algorithm 1: Full gradient ALUM Method |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | data points in australian dataset from LIBSVM [32]. |
| Dataset Splits | No | The paper uses the 'australian dataset from LIBSVM' but does not specify exact training, validation, or test splits (e.g., percentages or sample counts) or cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) used in their implementation. |
| Experiment Setup | Yes | For the Gaussian model, the potential is defined as: fi(x) = 1/2N (di - x) Σ^-1(di - x) , where di and Σ is generated randomly to satisfy d = 5,N = 100,m = 1 and L = 10. For logistic regression model, the potential is: f(x) = sum_i=1 log(1 + exp(-yiaix)) , where m is set such that κ = 10^4 and yi, ai are data points in australian dataset from LIBSVM [32]. We specify how we generate this reference path in Appendix D.1. The detailed setup can be found in Appendix D.2. |