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 [1].
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. |