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..
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. |