Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Authors: Xun Zheng, Bryon Aragam, Pradeep K. Ravikumar, Eric P. Xing
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,147 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,255 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compared our method against greedy equivalent search (GES) [9, 31], the PC algorithm [42], and Li NGAM [38]. ... In each experiment, a random graph G was generated from one of two random graph models, Erdös Rényi (ER) or scale-free (SF). ... We now examine our method for structure recovery, which is shown in Figure 3. ... In our experiments we generated random graphs with d = 10, and then generated 10 simulated datasets containing n = 20 samples (for high-dimensions) and n = 1000 (for low-dimensions). We then compared the scores returned by our method to the exact global minimizer computed by GOBNILP along with the estimated parameters. The results are shown in Table 1. |
| Researcher Affiliation | Collaboration | Xun Zheng1, Bryon Aragam1, Pradeep Ravikumar1, Eric P. Xing1,2 1Carnegie Mellon University 2Petuum Inc. EMAIL |
| Pseudocode | Yes | Algorithm 1 NOTEARS algorithm |
| Open Source Code | Yes | The implementation is publicly available at https://github.com/xunzheng/ notears. |
| Open Datasets | Yes | In each experiment, a random graph G was generated from one of two random graph models, Erdös Rényi (ER) or scale-free (SF). ... We also compared FGS and NOTEARS on a real dataset provided by Sachs et al. [33]. |
| Dataset Splits | No | The paper does not explicitly state the use of validation splits or a specific validation methodology like cross-validation. It mentions training models and evaluating on test sets but lacks details on validation splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions L-BFGS, PQN method, and GOBNILP but does not specify version numbers for these or any other software components. |
| Experiment Setup | Yes | For brevity, we outline the basic set-up of our experiments here; precise details of our experimental set-up, including all parameter choices and more detailed evaluations, can be found in Appendix E. |