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 Wasserstein Proximal Gradient Algorithm
Authors: Adil Salim, Anna Korba, Giulia Luise
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide numerical experiments with a ground truth target distribution µ 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 ('µ0 is Gaussian with m0 = 10 and σ0 = 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 γ = 0.1, and µ0 is Gaussian with m0 = 10 and σ0 = 100, in Figure 1. Note that λ = 1.0. |