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..
Stochastic Online Learning with Probabilistic Graph Feedback
Authors: Shuai Li, Wei Chen, Zheng Wen, Kwong-Sak Leung4675-4682
AAAI 2020 | Venue PDF | 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. |