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..
An Efficient High-dimensional Gradient Estimator for Stochastic Differential Equations
Authors: Shengbo Wang, Jose Blanchet, Peter W Glynn
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to establishing the validity of our methodology for general SDEs with jumps, we also perform numerical experiments that test our estimator in linear-quadratic control problems parameterized by high-dimensional neural networks. |
| Researcher Affiliation | Academia | Shengbo Wang MS&E Stanford University Stanford, CA 94305 EMAIL Jose Blanchet MS&E Stanford University Stanford, CA 94305 EMAIL Peter Glynn MS&E Stanford University Stanford, CA 94305 EMAIL |
| Pseudocode | No | The paper describes mathematical derivations and processes but does not include a clearly labeled "Pseudocode" or "Algorithm" block. |
| Open Source Code | Yes | Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: The code is submitted with the paper. |
| Open Datasets | No | Does the paper explicitly state that the dataset used in the experiments is publicly available or an open dataset? Answer: [No] |
| Dataset Splits | No | Does the paper explicitly provide training/test/validation dataset splits needed to reproduce the experiment? Answer: [No] |
| Hardware Specification | Yes | The computation time data was generated on a system equipped with a PCIE version of Nvidia Tesla V100 GPU, featuring 32GB of VRAM. Additionally, the system includes a 2-core CPU and 16GB of RAM |
| Software Dependencies | No | Does the paper provide a reproducible description of the ancillary software. A reproducible description must include specific version numbers for key software components? Answer: [No] |
| Experiment Setup | Yes | Does the paper explicitly provide details about the experimental setup, especially hyperparameters or system-level training settings? Answer:[Yes] Justification: The parameters of the control system in Section 4 are presented in the code. There is no hyperparameter that needs fine-tuning. |