Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Semiparametric Differential Graph Models
Authors: Pan Xu, Quanquan Gu
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,823 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,051 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Quanquan Gu University of Virginia EMAIL |
| 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]. |