Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
Authors: Baojian Zhou, Feng Chen, Yiming Ying
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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}. |