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..
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 | Venue PDF | 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. |