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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Weighted Model Integration Distributions
Authors: Paolo Morettin, Samuel Kolb, Stefano Teso, Andrea Passerini5224-5231
AAAI 2020 | Venue PDF | 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 EMAIL 2KU Leuven, Belgium EMAIL |
| 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. |