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 [1].
Learning CNF Theories Using MDL and Predicate Invention
Authors: Arcchit Jain, Clément Gautrais, Angelika Kimmig, Luc De Raedt
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that Mistle can learn CNF theories accurately and works well in tasks involving compression and classification. We consider the tasks of compression and classification, and the following questions: |
| Researcher Affiliation | Academia | Arcchit Jain1 , Cl ement Gautrais1 , Angelika Kimmig1 and Luc De Raedt1,2 1KU Leuven, Dept. of Computer Science; Leuven.AI, B-3000 Leuven, Belgium 2AASS, Orebro University, Sweden |
| Pseudocode | Yes | Algorithm 1 Mistle and Algorithm 2 compress(T) |
| Open Source Code | No | The paper mentions the software Mistle is written in and the third-party libraries it uses, but does not provide a link or explicit statement that the source code for Mistle itself is openly available. |
| Open Datasets | Yes | For this experiment, we use the UCI datasets [Coenen, 2003; Dua and Graff, 2017]. |
| Dataset Splits | Yes | To test whether Mistle s theories are not only highly compressed but also accurate, we compare Mistle with KRIMP on a classification task, using 10 fold cross-validation on the UCI datasets. |
| Hardware Specification | Yes | Experiment 1 is run on Intel i5 (2.3Ghz; 16Gb RAM) while experiments 2 and 3 are run on Intel i7 (3.4 GHz; 16Gb RAM). |
| Software Dependencies | No | Mistle is written in Python 3.7 and uses SPMF library [Fournier-Viger et al., 2016] for closed frequent itemset mining and Pico SAT library [Biere, 2008] for SAT solving. While Python 3.7 has a specific version, the libraries SPMF and Pico SAT do not have their version numbers explicitly stated in this sentence, only cited papers. |
| Experiment Setup | No | The paper does not provide specific details on hyperparameters, model initialization, or other system-level training settings. It only mentions the use of 10-fold cross-validation and the choice of minimum support threshold for KRIMP and Mistle. |