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..
Structured Graph Learning Via Laplacian Spectral Constraints
Authors: Sandeep Kumar, Jiaxi Ying, Jose Vinicius de Miranda Cardoso, Daniel Palomar
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive numerical experiments with both synthetic and real data sets demonstrate the effectiveness of the proposed methods. An R package containing code for all the experimental results is available at https://cran.r-project. org/package=spectral Graph Topology. |
| Researcher Affiliation | Academia | Sandeep Kumar EMAIL Jiaxi Ying EMAIL Jos e Vin ıcius de M. Cardoso EMAIL Daniel P. Palomar , EMAIL Department of Industrial Engineering and Data Analytics Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong |
| Pseudocode | Yes | Algorithm 1, which we denote by SGL, summarizes the implementation of the structured graph learning via Laplacian spectral constraints. |
| Open Source Code | Yes | An R package containing code for all the experimental results is available at https://cran.r-project. org/package=spectral Graph Topology. [...] An R package containing the code for all the simulations is made available as open source repository. |
| Open Datasets | Yes | We apply SGL to learn similarity graphs from a real categorical animal dataset [65] with binary entries [...] Herein, animals data set [65, 67] is taken into consideration to learn weighted graphs. [...] We consider the RNA-Seq Cancer Genome Atlas Research Network [66] data set available at the UC-Irvine Machine Learning Database [68]. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits (e.g., percentages, sample counts, or methods for creating them). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions an 'R package' for code but does not specify version numbers for R or any specific software libraries/dependencies used in the implementation. |
| Experiment Setup | Yes | Algorithm 1 Input: SCM S, k, c1, c2, β [...] The input for all the algorithms is the sample covariance matrix plus an identity matrix scaled by 1/3 (see [26]). [...] SGL with k = 1, and (d) SGL with k = 5. |