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.
Communication-Efficient Frank-Wolfe Algorithm for Nonconvex Decentralized Distributed Learning
Authors: Wenhan Xian, Feihu Huang, Heng Huang10405-10413
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,920 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,668 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on matrix completion and model compression applications demonstrate the efficiency of our new algorithm. |
| Researcher Affiliation | Collaboration | Wenhan Xian 1, Feihu Huang 1, Heng Huang 1,2 1 Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA 2 JD Finance America Corporation, Mountain View, CA, USA |
| Pseudocode | Yes | Algorithm 1 Decentralized Quantized Stochastic Frank Wolfe (DQSFW) |
| Open Source Code | No | The paper does not contain any statement about making its source code available or provide a link to a code repository. |
| Open Datasets | Yes | We run our experiment on two benchmark datasets, Movie Lens 100k and Movie Lens 1m (Harper and Konstan 2015). |
| Dataset Splits | No | For Movie Lens datasets, the paper states, 'we take all data for training'. For CIFAR-10, it mentions '50000 training samples' but does not specify a validation split or percentages for train/test/validation. |
| Hardware Specification | Yes | Each node is an Intel Xeon E5-2660 machine within an infiniband network. ... The experiment is implemented on 8 GTX1080 GPUs by Pytorch. |
| Software Dependencies | No | The paper mentions 'mpi4py' and 'Pytorch' as software used, but it does not specify version numbers for these dependencies. |
| Experiment Setup | Yes | For all of the three algorithms, we set step size ηt = t 0.75. ... For all three algorithms, step size is chosen as ηt = 1 2t 0.75. ... we use cross-entropy loss function as the criterion and set τ = 0.8. |