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..
Primal-Dual Block Generalized Frank-Wolfe
Authors: Qi Lei, JIACHENG ZHUO, Constantine Caramanis, Inderjit S. Dhillon, Alexandros G. Dimakis
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,407 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,958 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 | We empirically show that our algorithm outperforms the state-of-the-art methods on (multi-class) classi๏ฌcation tasks. |
| Researcher Affiliation | Collaboration | UT Austin Amazon {leiqi@oden., jzhuo@, constantine@, inderjit@cs., dimakis@austin.}utexas.edu |
| Pseudocode | Yes | Algorithm 1 Primal-Dual Block Generalized Frank-Wolfe Method for โ1 Norm Ball |
| Open Source Code | Yes | The codes to reproduce our results could be found in https://github.com/Carlson Zhuo/ primal_dual_frank_wolfe. |
| Open Datasets | Yes | The six datasets used here are summarized in Table 2. All of them can be found in LIBSVM datasets [4]. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| 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 | Yes | Algorithms are implemented in C++, with the Eigen linear algebra library [12]. |
| Experiment Setup | Yes | We set the โ1 constraint to be 300 and the โ2 regularize parameter to 10/n to achieve reasonable prediction accuracy. |