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..
Constraint-Free Structure Learning with Smooth Acyclic Orientations
Authors: Riccardo Massidda, Francesco Landolfi, Martina Cinquini, Davide Bacciu
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to being asymptotically faster, our empirical analysis highlights how COSMO performance on graph reconstruction compares favorably with competing structure learning methods. |
| Researcher Affiliation | Academia | Riccardo Massidda, Francesco Landolfi, Martina Cinquini, Davide Bacciu Department of Computer Science Università di Pisa, Italy EMAIL EMAIL |
| Pseudocode | No | The paper describes methods textually and with equations, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | For reproducibility purposes, we release the necessary code to replicate our experiments. [...] https://github.com/rmassidda/cosmo |
| Open Datasets | Yes | We base our empirical analysis on the testbed originally introduced by Zheng et al. (2018) and then adopted as a benchmark by all followup methods. [...] We include in our code the exact data generation process from the original implementation of NOTEARS.2 |
| Dataset Splits | No | Then, we test each configuration on five randomly sampled DAGs. We select the best hyperparameters according to the average AUC value. Finally, we perform a validation step by running the best configuration on five new random graphs. |
| Hardware Specification | Yes | We run all the experiments on our internal cluster of Intel(R) Xeon(R) Gold 5120 processors, totaling 56 CPUs per machine. |
| Software Dependencies | No | The paper mentions using Py Torch for automatic differentiation but does not provide a specific version number. No other software components or libraries are listed with version details. |
| Experiment Setup | Yes | For COSMO, we interrupt the optimization after 2000 epochs. For the non-linear version of DAGMA, we increased the maximum epochs to 7000. [...] In particular, we sampled the learning rate from the range (1e-4, 1e-2) and the regularization coefficients from the interval (1e-4, 1e-1). [...] For COSMO, we sample hyperparameters from the ranges in Table 4. |