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..
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
Authors: Baojian Zhou, Feng Chen, Yiming Ying
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to show the efficiency and effectiveness of the proposed algorithms. We conduct experiments on both synthetic and real datasets to evaluate the performance of GRAPHSTOIHT. |
| Researcher Affiliation | Academia | 1Department of Computer Science, SUNY at Albany, Albany, NY, USA 2Department of Mathematics and Statistics, SUNY at Albany, Albany, NY, USA. |
| Pseudocode | Yes | Algorithm 1 GRAPHSTOIHT |
| Open Source Code | Yes | The source code and datasets are accessible at: https: //github.com/baojianzhou/graph-sto-iht. |
| Open Datasets | Yes | We apply it to the breast cancer dataset in Van De Vijver et al. (2002), which contains 295 training samples including 78 positives (metastatic) and 217 negatives (non-metastatic). We use the Protein-Protein Interaction (PPI) network in Jacob et al. (2009)4. |
| Dataset Splits | Yes | All related parameters are tuned by 5-fold-cross-validation on each training dataset. We tune b and η on an additional validation dataset with 100 observations. |
| Hardware Specification | Yes | All experiments are tested on 56 CPUs of Intel Xeon(R) E5-2680 with 251GB of RAM. |
| Software Dependencies | No | All codes are written in Python and C language. No specific version numbers for software libraries, frameworks, or languages are provided. |
| Experiment Setup | Yes | All methods terminate when Axt+1 y 10 7 (corresponding to convergence ) or t/n 500 (corresponding to the maximum number of epochs allowed). For GRAPHSTOIHT, one epoch contains n iterations. We fix the sparsity s = 8 and n = 180 and try different b from set {1, 2, 4, 8, 16, 24, 32, 40, 48, 56, 64, 180}. We consider 16 different η from set {0.1, 0.2, . . . , 1.5, 1.6}. |