Learning Weighted Model Integration Distributions
Authors: Paolo Morettin, Samuel Kolb, Stefano Teso, Andrea Passerini5224-5231
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results with synthetic and real-world data highlight the promise of the approach. |
| Researcher Affiliation | Academia | 1University of Trento, Italy {paolo.morettin, stefano.teso, andrea.passerini}@unitn.it 2KU Leuven, Belgium samuel.kolb@cs.kuleuven.be |
| Pseudocode | Yes | Algorithm 1 The inner loop of the INCAL+ algorithm. |
| Open Source Code | No | The code is available at: URL ANONYMIZED |
| Open Datasets | Yes | We evaluated LARIAT on the hybrid UCI benchmarks contained in the MLC++ library, which includes 18 hybrid datasets from different real-world domains. |
| Dataset Splits | Yes | For each configuration, we generated 20 different ground-truth models and relative dataset, each consisting of 500 training and 50 validation examples. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact CPU/GPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Math SAT solver' and 'Z3' but does not provide specific version numbers for these software dependencies used in their experiments. |
| Experiment Setup | Yes | we implemented INCAL+ using the Math SAT solver, applied it to the synthetic datasets with a timeout of 300 seconds for each call, and measured the misclassification error between the true and the learned support. |