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].
A Computationally Efficient Method for Learning Exponential Family Distributions
Authors: Abhin Shah, Devavrat Shah, Gregory Wornell
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work is theoretical in nature. |
| Researcher Affiliation | Academia | Abhin Shah MIT EMAIL Devavrat Shah MIT EMAIL Gregory W. Wornell MIT EMAIL |
| Pseudocode | Yes | Algorithm 1: Projected Gradient Descent |
| Open Source Code | No | The paper states that it is theoretical in nature and does not conduct experiments. Therefore, it does not provide open-source code for its methodology. |
| Open Datasets | No | The paper states that it is theoretical in nature and does not conduct experiments. It does not mention using any datasets, public or otherwise, for training or evaluation. |
| Dataset Splits | No | The paper states that it is theoretical in nature and does not conduct experiments. It does not provide any training/test/validation dataset splits. |
| Hardware Specification | No | The paper states that it is theoretical in nature and does not conduct experiments. Therefore, it does not describe any specific hardware used. |
| Software Dependencies | No | The paper states that it is theoretical in nature and does not conduct experiments. Therefore, it does not provide specific software dependencies or version numbers. |
| Experiment Setup | No | The paper states that it is theoretical in nature and does not conduct experiments. Therefore, it does not provide specific experimental setup details like hyperparameters or training settings. |