Semiparametric Differential Graph Models
Authors: Pan Xu, Quanquan Gu
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough experiments on both synthetic and real world data support our theory. |
| Researcher Affiliation | Academia | Pan Xu University of Virginia px3ds@virginia.edu Quanquan Gu University of Virginia qg5w@virginia.edu |
| Pseudocode | No | The paper describes algorithms such as proximal gradient descent but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of their own methodology. |
| Open Datasets | Yes | We applied our approach to the same gene expression data used in [38], which were collected from patients with stage III or IV ovarian cancer. |
| Dataset Splits | Yes | To choose the tuning parameters λ and b, we adopt 5-fold cross-validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'graphical Lasso' and the 'huge package' but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | To choose the tuning parameters λ and b, we adopt 5-fold cross-validation. The LDGM-MCP and LDGM-L1 estimators are obtained by solving the proximal gradient descent algorithm [4]. |