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 Latent Graph Structures and their Uncertainty
Authors: Alessandro Manenti, Daniele Zambon, Cesare Alippi
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results validate our theoretical claims and demonstrate the effectiveness of the proposed approach. ... 6. Experiments |
| Researcher Affiliation | Academia | 1Universit a della Svizzera italiana, IDSIA, Lugano, Switzerland 2Politecnico di Milano, Milan, Italy. |
| Pseudocode | No | The paper describes methods and algorithms but does not provide any explicitly labeled pseudocode or algorithm blocks with structured formatting. |
| Open Source Code | Yes | 1Code available at https://github.com/allemanenti/Learning-Calibrated-Structures |
| Open Datasets | Yes | To demonstrate that our method learns meaningful graph distributions in real-world settings, we train a neural network on air quality data in Beijing (Zheng et al., 2013). |
| Dataset Splits | Yes | We result in a dataset of 35k input-output pairs (x, y), 80% of which are used as training set, 10% as validation set, and the remaining 10% as test set. |
| Hardware Specification | Yes | The paper s experiments were run on a workstation with AMD EPYC 7513 processors and NVIDIA RTX A5000 GPUs; on average, a single model training terminates in a few minutes with a memory usage of about 1GB. |
| Software Dependencies | No | The developed code relies on Py Torch (Paszke et al., 2019) and the following additional open-source libraries: Py Torch Geometric (Fey & Lenssen, 2019), Num Py (Harris et al., 2020) and Matplotlib (Hunter, 2007). While these libraries are mentioned, specific version numbers (e.g., PyTorch 1.9) are not provided. |
| Experiment Setup | Yes | The model is trained using Adam optimizer (Kingma & Ba, 2014) with parameters β1 = 0.9, β2 = 0.99. Where not specified, the learning rate is set to 0.05 and decreased to 0.01 after 5 epochs. We grouped data points into batches of size 128. Initial values of θ are independently sampled from the U(0.0, 0.1) uniform distribution. |