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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Projection-free Distributed Online Convex Optimization with $O(\sqrtT)$ Communication Complexity
Authors: Yuanyu Wan, Wei-Wei Tu, Lijun Zhang
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 18,926 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,306 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, we perform simulation experiments to verify the performance of our proposed algorithms. 5.1. Experimental Settings 5.2. Experimental Results |
| Researcher Affiliation | Collaboration | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 24Paradigm Inc., Beijing, China. |
| Pseudocode | Yes | Algorithm 1 CGSC; Algorithm 2 D-BOCG; Algorithm 3 D-BBCG |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | As in Zhang et al. (2017), we also use the aloi dataset from the LIBSVM repository (Chang & Lin, 2011), the details of which are summarized in Table 1. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments are provided. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | According to Zhang et al. (2017), we set the bound of trace norm as τ = 50, and set st = 1/ t and η = c T 3/4 for D-OCG by tunning the constant c. For our D-BOCG, we set K = T , ϵ = 1e 5, L = 20 and η = c T 3/4 by tunning the constant c. For both D-BOCG and D-OCG, the constant c is selected from [0.01, , 1e5]. ... For D-BBCG, we set K = T , ϵ = 1e 5, L = 20, δ = 0.1 and η = c T 3/4 by selecting the constant c from [0.01, , 1e5]. |