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..
Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning
Authors: Michael Abseher, Frederico Dusberger, Nysret Musliu, Stefan Woltran
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report on extensive experiments in different problem domains which show a significant speedup when choosing the tree decomposition according to this concept over simply using an arbitrary one of the same width. |
| Researcher Affiliation | Academia | Institute of Information Systems 184/2 Vienna University of Technology Favoritenstraße 9 11, 1040 Vienna, Austria |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to the source code for the methodology described. |
| Open Datasets | Yes | The full training dataset used for our experiments is available under the following link: www.dbai.tuwien.ac.at/research/project/dflat/ features/training.zip |
| Dataset Splits | Yes | The results that we present in this paper are obtained with parameter settings that were chosen based on several of our previous experiments using 10-fold cross validation. |
| Hardware Specification | Yes | All our experiments were performed on a single core of an AMD Opteron 6308@3.5GHz processor running Debian GNU/Linux 7 (kernel 3.2.0-4-amd64) |
| Software Dependencies | Yes | We evaluate our approach using two recently developed DP solvers, D-FLAT (v. 1.0.1) and SEQUOIA (v. 0.9). The subsequent machine learning tasks were carried out with WEKA 3.6.11 [Hall et al., 2009]. |
| Experiment Setup | No | The paper mentions that parameter settings were chosen based on previous experiments and 10-fold cross-validation, but it does not provide specific hyperparameter values or detailed system-level training configurations for the models used (e.g., learning rates, batch sizes, optimizer details). |