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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
The Wasserstein Proximal Gradient Algorithm
Authors: Adil Salim, Anna Korba, Giulia Luise
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,495 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,095 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide numerical experiments with a ground truth target distribution ยต to illustrate the dynamical behavior of the FB scheme, similarly to [34, Section 4.1]. |
| Researcher Affiliation | Academia | Adil Salim Visual Computing Center KAUST EMAIL Anna Korba Gatsby Computational Neuroscience Unit University College London EMAIL Giulia Luise Computer Science Department University College London EMAIL |
| Pseudocode | No | The paper describes the Forward Backward Euler scheme using mathematical equations (17) and (18) but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper describes numerical experiments using generated Gaussian distributions ('ยต0 is Gaussian with m0 = 10 and ฯ0 = 100') rather than a named, publicly accessible dataset with explicit access information or citation. |
| Dataset Splits | No | The paper describes numerical simulations to illustrate the scheme's behavior but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions running 'numerical experiments' but does not provide any specific details about the hardware used (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, specific libraries). |
| Experiment Setup | Yes | We consider F(x) = 0.5|x|2, and H the negative entropy. [...] This allows to show the dynamical behavior of the FB scheme when ฮณ = 0.1, and ยต0 is Gaussian with m0 = 10 and ฯ0 = 100, in Figure 1. Note that ฮป = 1.0. |