Variance estimation in compound decision theory under boundedness
Authors: Subhodh Kotekal
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper does not include experiments. Justification: The paper does not include experiments. |
| Researcher Affiliation | Academia | Subhodh Kotekal Department of Statistics University of Chicago Chicago, IL 60637 skotekal@uchicago.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any statement about providing open-source code for the methodology described. The NeurIPS checklist indicates no experiments and thus no code. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for empirical studies, training, or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |