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..
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
Authors: Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show via numerical studies that the proposed estimator outperforms other popular methods for estimating Gaussian graphical models. |
| Researcher Affiliation | Collaboration | 1Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada 2School of Statistics, University of Minnesota, Minneapolis, MN, USA 3Tencent AI Lab, Tencent Technology, Shenzhen, China. |
| Pseudocode | Yes | Algorithm 1 A sequential convex approximation for the graphical nonconvex optimization. |
| Open Source Code | No | The paper states that 'Algorithm 1 can be implemented using existing R packages such as glasso.' but does not provide any link or explicit statement about releasing its own source code. |
| Open Datasets | No | The paper states, 'Finally, we generate the data according to X(1), . . . , X(n) i.i.d. N(0, Σ),' indicating that synthetic data was used, not a publicly available dataset. |
| Dataset Splits | No | The paper describes generating synthetic data and evaluating methods, but it does not specify any training, validation, or test dataset splits or cross-validation setups. It states, 'We present the results averaged over 100 data sets for each of the two simulation settings'. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, memory, or specific computing infrastructure used for the experiments. |
| Software Dependencies | No | The paper mentions that 'Algorithm 1 can be implemented using existing R packages such as glasso,' but it does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For our proposal, we consider T = 4 iterations with the SCAD penalty proposed by Fan & Li (2001)... In all of our simulation studies, we pick γ = 2.1. |