DAGs with No Curl: An Efficient DAG Structure Learning Approach
Authors: Yue Yu, Tian Gao, Naiyu Yin, Qiang Ji
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental studies on benchmark datasets demonstrate that our method provides comparable accuracy but better efficiency than baseline DAG structure learning methods on both linear and generalized structural equation models, often by more than one order of magnitude. |
| Researcher Affiliation | Collaboration | 1Department of Mathematics, Lehigh University, Bethlehem, PA. 2IBM Research, Yorktown Heights, NY. 3Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY. |
| Pseudocode | Yes | Algorithm 1 DAG-No Curl algorithm |
| Open Source Code | Yes | The code will be publicly released at https: //github.com/fishmoon1234/DAG-No Curl. |
| Open Datasets | Yes | We first test our algorithm in linear synthetic datasets. We employ similar experimental setups as existing works (Zheng et al., 2018). In each experiment, a random graph G is generated by using the Erd os R enyi (ER) model or the scale-free (SF) model with k expected edges... Given A0, we take n = 1000 i.i.d. samples of X... We also test nonlinear SEM and real datasets in Section 5.5 and 5.6. ... a real-world bioinformatics dataset (Sachs et al., 2005) for the discovery of a protein signaling network... |
| Dataset Splits | No | The paper mentions generating 1000 i.i.d. samples and discusses training, but does not explicitly specify a split into training, validation, and test sets with percentages or counts. |
| Hardware Specification | No | The paper mentions "CPU time" as a metric but does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions using "L-BFGS (Liu and Nocedal, 1989)", "PyTorch (Paszke et al., 2017)", and "Adam (Kingma and Ba, 2015)", but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We test many sets of hyperparameters and due to the page limit the full numerical results are listed in Table 4 for ER3-Gaussian and Table 5 for ER6-Gaussian cases in the supplement. It is observed that as long as λ ≥ 10, the accuracy results are all satisfactory. Among which, λ = 10^2 and λ = (10, 10^3) are generally the best values in term of both accuracy and computational efficiency. Hence, we select them as the default values and refer them as No Curl-1 and No Curl-2, respectively. ... we use a fixed threshold value ϵ = 0.3 as suggested by NOTEARS (Zheng et al., 2018). |