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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Approximate Conditional Gradient Descent on Multi-Class Classification
Authors: Zhuanghua Liu, Ivor Tsang
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results verify that our method outperforms the state-of-the-art stochastic projectionfree methods. |
| Researcher Affiliation | Academia | Zhuanghua Liu, Ivor Tsang Centre for Artifical Intelligence University of Technology Sydney EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Approximate Frank-Wolfe |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | We conducted our experiment on several large-scale datasets from the libsvm website1. The datasets are summarized in the following table: [...] 1https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper mentions using "training data" and "datasets" for evaluation but does not specify clear train/validation/test splits, percentages, or absolute sample counts for data partitioning. No mention of a "validation" set. |
| Hardware Specification | Yes | Our experiments run on a server with 3.1 GHZ CPU and 132 GB memory. |
| Software Dependencies | No | Our algorithm and baseline methods are implemented in Matlab. (No version number for Matlab is provided.) |
| Experiment Setup | Yes | In the evaluation of approximate Frank Wolfe, we set ν = 50, δ = 10 and k0 = 100. For fair comparison, we use the default parameter for SFW and SVRF from (Hazan and Luo 2016), i.e., The size of mini-batches at round t is t2, t for SFW and SVRF, respectively. |