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.
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Authors: Zebang Shen, Aryan Mokhtari, Tengfei Zhou, Peilin Zhao, Hui Qian
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 18,976 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,077 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 convex minimization and AUC-maximization validate the ef๏ฌciency of our method. and In this section, we evaluate the empirical performance of DSBA and compare it with several state-of-the-art methods including: DSA, EXTRA, SSDA, and DLM. |
| Researcher Affiliation | Collaboration | 1Zhejiang University 2Tencent AI Lab 3Massachusetts Institute of Technology 4South China University of Technology. |
| Pseudocode | Yes | Algorithm 1 DSBA for node n and Algorithm 2 Computation on node 0 at iteration t |
| Open Source Code | No | The paper does not provide concrete access to source code or a statement about its public availability. |
| Open Datasets | Yes | As to dataset, we use News20-binary, RCV1, and Sector from LIBSVM dataset |
| Dataset Splits | No | The paper states it "randomly split the them into N partitions with equal sizes" but does not provide specific train/validation/test split percentages, sample counts, or references to predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific details regarding software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | We tune the step size of all algorithms and select the ones that give the best performance. and The โ2-regularization parameter ฮป is set to 1/(10Q) in all cases. |