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.
A Near-Optimal Algorithm for Decentralized Convex-Concave Finite-Sum Minimax Optimization
Authors: Hongxu Chen, Ke Wei, Haishan Ye, Luo Luo
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 40,178 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,863 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 | In this section, numerical experiments are conducted to evaluate the performance of Algorithm 2. Numerical experiments demonstrate that our algorithm outperforms existing methods in practice. |
| Researcher Affiliation | Collaboration | 1School of Data Science, Fudan University 2School of Management, Xi an Jiaotong University 3SGIT AI Lab, State Grid Corporation of China 4Shanghai Key Laboratory for Contemporary Applied Mathematics |
| Pseudocode | Yes | Algorithm 1 Fast Mix(U0, W, R) Algorithm 2 DIVERSE |
| Open Source Code | Yes | The code is included in the supplementary material. |
| Open Datasets | Yes | The numerical experiments are conducted on datasets a9a, w8a, ijcnn1, and cod-rna, from the LIBSVM repository [14]. |
| Dataset Splits | No | The paper mentions distributing samples across nodes but does not specify how the overall datasets (a9a, w8a, ijcnn1, cod-rna) are split into training, validation, or test sets for the experiments. |
| Hardware Specification | No | It requires few computational resources to run the numerical experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software components or libraries used in the experiments. |
| Experiment Setup | Yes | The regularization parameters are set to be r1 = r2 = 0.2. The parameters of these algorithms are set according to the theoretical analysis or the recommended settings by the authors [39, 46, 55]. Specifically, the parameter b in the DIVERSE is set to be 128 and the fixed batch size for each node in OADSVI is set to be 3. The best performance step sizes from {0.1, 0.05, 0.01} are used, up to the algorithms and the datasets. |