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..
Asynchronous Decentralized Online Learning
Authors: Jiyan Jiang, Wenpeng Zhang, Jinjie GU, Wenwu Zhu
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that AD-OGP runs significantly faster than its synchronous counterpart and also verify the theoretical results. |
| Researcher Affiliation | Collaboration | Jiyan Jiang Tsinghua University EMAIL Wenpeng Zhang Ant Group EMAIL Jinjie Gu Ant Group EMAIL Wenwu Zhu Tsinghua University EMAIL |
| Pseudocode | Yes | Protocol 1 Asynchronous Decentralized Online Convex Optimization (AD-OCO) ... Algorithm 1 Asynchronous Decentralized Online Gradient-Push (AD-OGP) |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We select two large-scale real-world datasets. (i) The higgs dataset is a benchmark dataset in highenergy physics [4] for binary classification, which consists of 11 million instances with 28 features. (ii) The poker-hand dataset is a commonly used dataset in automatic rule generation [6, 7] for 10-class classification, which has 1 million instances with 25 features. |
| Dataset Splits | No | The paper mentions using 'higgs' and 'poker-hand' datasets but does not specify exact split percentages or sample counts for training, validation, or test sets, nor does it reference predefined splits with citations. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | For binary classification, we use the logistic loss; for multi-class classification, we use the multivariate logistic loss [11] (see detailed definitions in supplementary materials). ... Moreover, we adopt the commonly used L2-norm balls as decision sets and set their diameters as 100. The learning rate η is set as what the corresponding theory suggests (see more details in supplementary materials). |