On The Projection Operator to A Three-view Cardinality Constrained Set
Authors: Haichuan Yang, Shupeng Gui, Chuyang Ke, Daniel Stefankovic, Ryohei Fujimaki, Ji Liu
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section will validate the proposed method on both synthetic data and two practical applications: crowdsourcing and identification of gene regulatory networks. |
| Researcher Affiliation | Collaboration | 1University of Rochester, Rochester, NY, USA 2NEC, Cupertino, CA, USA. |
| Pseudocode | Yes | Algorithm 1: Iterative Hard Thresholding. Input: Sparsity parameter s. Result: Problem solution wt. and Algorithm 2: Gradient Matching Pursuit. Input: Sparsity parameter s. Result: Problem solution wt. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions using synthetic data and data generated with Gene Net Weaver, but it does not provide concrete access information (link, DOI, or specific citation with authors/year) for the datasets used in their experiments. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits needed for reproduction. It mentions 'samples used in training' and 'samples used in testing' but not specific splits like percentages or counts for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with versions) needed to replicate the experiments. |
| Experiment Setup | Yes | p is fixed as 400 and n is gradually increased. The group sparsity upper bounds sg for g 2 G1 and g 2 G2 are uniformly generated from the integers in the range[1, pp]. The overall sparsity upper bound is set by 0.8 min(Pg2G2 sg). and we generate the quality matrix Q from uniformly random distribution with interval [0.5, 0.9]. The prior probability P(yj = 1) and P(yj = 0) are set as 0.5 for all the tasks. and we control the size of gene network to be N = 30 vertexes and the gene expression data are generated under 10% Gaussian white noise. |