Constraint-Free Structure Learning with Smooth Acyclic Orientations
Authors: Riccardo Massidda, Francesco Landolfi, Martina Cinquini, Davide Bacciu
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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 {riccardo.massidda,francesco.landolfi,martina.cinquini}@phd.unipi.it davide.bacciu@unipi.it |
| 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. |