A Computationally Efficient Method for Learning Exponential Family Distributions
Authors: Abhin Shah, Devavrat Shah, Gregory Wornell
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our work is theoretical in nature. |
| Researcher Affiliation | Academia | Abhin Shah MIT abhin@mit.edu Devavrat Shah MIT devavrat@mit.edu Gregory W. Wornell MIT gww@mit.edu |
| 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. |