A Core-Guided Approach to Learning Optimal Causal Graphs
Authors: Antti Hyttinen, Paul Saikko, Matti Järvisalo
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare the performance of Dseptor to those of state-of-art Max SAT solvers using the encoding of Hyttinen et al. [2014]. The experiments were run on 2.83-GHz Intel Xeon E5440 machines with 32-GB RAM and Debian GNU/Linux. First we compare the performance of the solvers on synthetic data generated from 7-node cyclic (possibly) linear Gaussian models with correlated disturbances. ... Figure 4 gives a comparison of Dseptor with the Max SAT solvers ... Dseptor outperforms all of the other solvers, solving 96% of the instances under the timeout of 600 seconds. |
| Researcher Affiliation | Academia | Antti Hyttinen and Paul Saikko and Matti J arvisalo HIIT, Department of Computer Science, University of Helsinki, Finland |
| Pseudocode | Yes | Alg. 1 outlines this approach as FINDCOREINCREMENTAL. ... Algorithm 1: Incremental core extraction in Dseptor. |
| Open Source Code | No | An implementation and an online appendix are available at the authors homepages." - This statement is too general and does not provide a specific, direct link to the source code for the methodology described in the paper, nor does it explicitly state the code is publicly released in a persistent repository. |
| Open Datasets | Yes | To examine the performances on more realistic sets of (in)dependence constraints, we looked at real-world datasets often used for benchmarking exact Bayesian network structure learning algorithms [Yuan and Malone, 2013; Bartlett and Cussens, 2017]." and Table 2 lists datasets such as "Adult", "Alarm", "Autos", "Bands", etc. |
| Dataset Splits | No | The paper mentions using N=1000 samples to obtain weights for independence constraints and refers to real-world datasets, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts) for reproducing its experiments. |
| Hardware Specification | Yes | The experiments were run on 2.83-GHz Intel Xeon E5440 machines with 32-GB RAM and Debian GNU/Linux. |
| Software Dependencies | No | We use Mini SAT [E en and S orensson, 2004] and IBM CPLEX as the internal SAT and IP solvers, respectively." - Specific version numbers for Mini SAT and IBM CPLEX are not provided. |
| Experiment Setup | Yes | We used BIC-based model selection to obtain the weights for the independence constraints over N = 1000 samples." and "We employed the BDEU score with equivalent sample size 10 to obtain constraint weights for this discrete data, and used a per-instance timeout of 7200 seconds." and "The edges were sampled at random to obtain average degree 2, coefficients were from [0.2, 0.8]. |