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..
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. |