An Efficient High-dimensional Gradient Estimator for Stochastic Differential Equations
Authors: Shengbo Wang, Jose Blanchet, Peter W Glynn
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 shengbo.wang@stanford.edu Jose Blanchet MS&E Stanford University Stanford, CA 94305 jose.blanchet@stanford.edu Peter Glynn MS&E Stanford University Stanford, CA 94305 glynn@stanford.edu |
| 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. |