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].
Understanding the Eluder Dimension
Authors: Gene Li, Pritish Kamath, Dylan J Foster, Nati Srebro
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Since this is a theory paper regarding several notions of complexity for learning problems, we do not foresee any immediate potential negative impacts. |
| Researcher Affiliation | Collaboration | Gene Li Toyota Technological Institute at Chicago EMAIL Pritish Kamath Google Research EMAIL Dylan J. Foster Microsoft Research EMAIL Nathan Srebro Toyota Technological Institute at Chicago EMAIL |
| Pseudocode | No | The paper is theoretical and focuses on mathematical definitions, theorems, and proofs. It does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is a theoretical work focusing on mathematical proofs and relationships, and does not involve any experimental implementations or code development for its main contributions. The checklist explicitly states '[N/A]' for questions related to code and experiments. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets. Therefore, there is no mention of dataset availability for training. The checklist explicitly states '[N/A]' for questions related to experiments. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving datasets. Therefore, there is no mention of training/test/validation dataset splits. The checklist explicitly states '[N/A]' for questions related to experiments. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments that would require specific hardware. The checklist explicitly states '[N/A]' for questions related to running experiments, including hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments that would require specific software dependencies with version numbers. The checklist explicitly states '[N/A]' for questions related to running experiments. |
| Experiment Setup | No | The paper is theoretical and does not report on experiments. Therefore, it does not describe any experimental setup details such as hyperparameters or training settings. The checklist explicitly states '[N/A]' for questions related to running experiments. |