Stochastic Online Learning with Probabilistic Graph Feedback
Authors: Shuai Li, Wei Chen, Zheng Wen, Kwong-Sak Leung4675-4682
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We analyze the asymptotic lower bounds and design algorithms in both cases. The regret upper bounds of the algorithms match the lower bounds with high probability. |
| Researcher Affiliation | Collaboration | 1Shanghai Jiao Tong University, 2Microsoft Research, 3Deep Mind, 4The Chinese University of Hong Kong |
| Pseudocode | Yes | Algorithm 1 One-Step Uniform Case; Algorithm 2 Cascade Case |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code for the described methodology is publicly available. |
| Open Datasets | No | The paper discusses theoretical reward distributions and stochastic online learning but does not use or provide access to specific datasets for training. |
| Dataset Splits | No | The paper does not describe any training, validation, or test dataset splits, as it focuses on theoretical analysis rather than empirical experiments. |
| Hardware Specification | No | No specific hardware (e.g., GPU, CPU models, or cloud resources) used for computation or experiments is mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies or their version numbers (e.g., programming languages, libraries, or frameworks) are mentioned in the paper. |
| Experiment Setup | No | No specific experimental setup details, such as hyperparameters or training configurations, are provided, as the paper is theoretical. |