Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality
Authors: Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 tvmarinov@google.com Mehryar Mohri Google Research & Courant Institute mohri@google.com Julian Zimmert Google Research zimmert@google.com |
| 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. |