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.
Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality
Authors: Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,281 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,218 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 | This work is theoretical and it is hard to judge societal impact. If you are including theoretical results... Did you state the full set of assumptions of all theoretical results? [Yes] Please refer to the results in the stated theorems. Did you include complete proofs of all theoretical results? [Yes] Please refer to the appendix for complete proofs of the main results. If you ran experiments... [N/A] There is no code available for this work. |
| Researcher Affiliation | Collaboration | Teodor V. Marinov Google Research EMAIL Mehryar Mohri Google Research & Courant Institute EMAIL Julian Zimmert Google Research EMAIL |
| Pseudocode | Yes | Algorithm 1: Algorithm based on LP3 |
| Open Source Code | No | There is no code available for this work. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets. It defines theoretical assumptions about reward distributions (e.g., Gaussian or sub-Gaussian) but does not use or provide access to any specific dataset for training. |
| Dataset Splits | No | The paper does not describe any training, validation, or test dataset splits as it is a theoretical paper and does not involve empirical experiments. |
| Hardware Specification | No | The paper is theoretical and does not describe hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers as it does not report empirical experiments. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |