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..
Learning to Discover Sparse Graphical Models
Authors: Eugene Belilovsky, Kyle Kastner, Gael Varoquaux, Matthew B. Blaschko
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluations focus on the challenging high dimensional settings in which p > n and consider both synthetic data and real data from genetics and neuroimaging. |
| Researcher Affiliation | Academia | 1KU Leuven 2INRIA 3University of Paris-Saclay 4University of Montreal. |
| Pseudocode | Yes | Algorithm 1 Training a GGM edge estimator |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for the source code of the methodology. |
| Open Datasets | Yes | We use the ABIDE dataset (Di Martino et al, 2014), a large scale resting state f MRI dataset. |
| Dataset Splits | Yes | Each network is trained continously with new samples generated until the validation error saturates. |
| Hardware Specification | No | We compute the average execution time of our method compared to Graph Lasso and BDGraph on a CPU in Table 3. |
| Software Dependencies | No | No specific version numbers are provided for software components like scikit-learn or the R-packages, only names and citations. |
| Experiment Setup | Yes | We train networks taking in 39, 50, and 500 node graphs. ... In all cases we have 50 feature maps of 3 3 kernels. The 39 and 50 node network with 6 convolutional layers and dk = k + 1. For the 500 node network with 8 convolutional layers and dk = 2k+1. We use Re LU activations. The last layer has 1 1 convolution and a sigmoid outputing a value of 0 to 1 for each edge. ... The networks are optimized using ADAM (Kingma & Ba, 2015) coupled with cross-entropy loss as the objective function (cf. Sec. 2.1). We use batch normalization at each layer. |