Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Adaptive Oracle-Efficient Online Learning
Authors: Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob D. Abernethy
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 19,732 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,252 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Did you run experiments? [N/A] |
| Researcher Affiliation | Academia | Guanghui Wang , Zihao Hu , Vidya Muthukumar , , Jacob Abernethy College of Computing School of Electrical and Computer Engineering School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30339 EMAIL |
| Pseudocode | Yes | Algorithm 1 Generalized follow-the-perturbed-leader with small-loss bound" and "Algorithm 3 Oracle-efficient Flipflop (OFF) |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not report experimental results that would involve training on specific datasets. The checklist explicitly states 'N/A' for questions related to experiments and data. |
| Dataset Splits | No | The paper is theoretical and does not report experimental results that would involve dataset splits. The checklist explicitly states 'N/A' for questions related to experiments and data. |
| Hardware Specification | No | The paper is theoretical and does not report experimental results, hence no hardware specifications are provided. The checklist explicitly states 'N/A' for questions about computing resources. |
| Software Dependencies | No | The paper is theoretical and does not report experimental results. Therefore, no specific software versions or dependencies are mentioned for reproducibility. The checklist explicitly states 'N/A' for questions about training details. |
| Experiment Setup | No | The paper is theoretical and does not report experimental results, so no specific experimental setup details like hyperparameters or training configurations are provided. The checklist explicitly states 'N/A' for questions about training details. |