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..
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
Authors: Kevin Bello, Bryon Aragam, Pradeep Ravikumar
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide extensive experiments for linear and nonlinear SEMs and show that our approach can reach large speedups and smaller structural Hamming distances against state-of-the-art methods. |
| Researcher Affiliation | Academia | Booth School of Business, University of Chicago, Chicago, IL 60637 Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213 |
| Pseudocode | Yes | Algorithm 1 DAGMA |
| Open Source Code | Yes | Code implementing the proposed method is open-source and publicly available at https://github.com/kevinsbello/dagma. |
| Open Datasets | No | For each d, 30 matrices were randomly sampled from a standard Gaussian distribution. Given a data matrix X = [x1, . . . , xd] Rn d, we define a score function Q(f; X) to measure the quality of a candidate SEM as follows: Q(f; X) = Pd j=1 loss(xj, fj(X)) For linear models. In Appendix C.1, we report results for linear SEMs with Gaussian, Gumbel, and exponential noises, and use the least squares loss. This implies data is simulated/generated, not a fixed publicly available dataset they are using. No access information is provided for generated data. |
| Dataset Splits | No | No explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or specific split methodologies) are mentioned in the paper. The paper implies data is generated and used for optimization. |
| Hardware Specification | Yes | All experiments were performed on a cluster running Ubuntu 18.04.5 LTS with Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz, and NVIDIA Tesla V100 GPU. |
| Software Dependencies | Yes | For our proposed method DAGMA, we implemented it in Python 3.8 and PyTorch 1.10.0. We use Adam [24] for optimization. |
| Experiment Setup | Yes | For all linear and nonlinear SEM experiments, we set the number of iterations T = 10000, initial central path coefficient µ(0) = 1, decay factor α = 0.5, ℓ1 parameter β1 = 0.01, log-det parameter s = 1.0. We use Adam optimizer with learning rate 0.001. |