DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
Authors: Kevin Bello, Bryon Aragam, Pradeep Ravikumar
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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. |