Optimal Underdamped Langevin MCMC Method
Authors: Zhengmian Hu, Feihu Huang, Heng Huang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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. |