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..
The Randomized Midpoint Method for Log-Concave Sampling
Authors: Ruoqi Shen, Yin Tat Lee
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Numerical Experiments In this section, we compare the algorithm from our paper, randomized midpoint method, with the one from [10]. We test the algorithms on the liver-disorders dataset and the breast-cancer dataset from UCL machine learning [17]. In both datasets, we observe a set of independent samples {xi, yi}m i=1, where yi is the label, xi is the feature and m is the number of samples. We sample from the target distribution p (θ) exp ( f(θ)) , where P m i=1 log exp yix T i θ + 1 , for regularization parameters λ. We set λ to be 10 2 in our experiments. Figure 1 shows the error of randomized midpoint method and the algorithm from [10] with different step size h. |
| Researcher Affiliation | Collaboration | Ruoqi Shen University of Washington EMAIL Yin Tat Lee University of Washington and Microsoft Research EMAIL |
| Pseudocode | Yes | Algorithm 1 Randomized Midpoint Method for ULD |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We test the algorithms on the liver-disorders dataset and the breast-cancer dataset from UCL machine learning [17]. |
| Dataset Splits | No | The paper mentions using datasets for numerical experiments but does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or explicit splitting methodology). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper describes numerical experiments but does not specify any software dependencies (e.g., libraries, frameworks, or programming languages) with their corresponding version numbers. |
| Experiment Setup | Yes | We set λ to be 10 2 in our experiments. |