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..
Stein Self-Repulsive Dynamics: Benefits From Past Samples
Authors: Mao Ye, Tongzheng Ren, Qiang Liu
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform extensive empirical studies of our new algorithm, showing that our method yields much higher sample ef๏ฌciency and better uncertainty estimation than vanilla Langevin dynamics. |
| Researcher Affiliation | Academia | Tongzheng Ren * UT Austin EMAIL Qiang Liu UT Austin EMAIL |
| Pseudocode | No | The paper describes algorithms using mathematical equations and textual explanations, but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Our code is available at https://github. com/lushleaf/Stein-Repulsive-Dynamics. |
| Open Datasets | Yes | We test the performance of SRLD on sampling the posterior of Bayesian Neural Network on the UCI datasets [Dua and Graff, 2017]. |
| Dataset Splits | No | All of the datasets are randomly partitioned into 90% for training and 10% for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU or GPU models used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or framework versions). |
| Experiment Setup | Yes | We assume the output is normal distributed, with a two-layer neural network with 50 hidden units and tanh activation to predict the mean of outputs. All of the datasets are randomly partitioned into 90% for training and 10% for testing. The results are averaged over 20 random trials. We refer readers to Appendix C for hyper-parameter tuning and other experiment details. |