Statistical Regimes and Runtime Prediction
Authors: Barry Hurley, Barry O'Sullivan
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Supported by a large-scale empirical study employing many years of industrial SAT Competition instances including repeated runs, we present statistical and empirical evidence that such a performance variation phenomenon necessitates a change in the evaluation of portfolio, runtime prediction, and automated configuration methods. |
| Researcher Affiliation | Academia | Barry Hurley and Barry O Sullivan Insight Centre for Data Analytics, University College Cork, Ireland |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper states that the dataset is available at a URL, but it does not provide an explicit statement or link for the open-source code of the methodology described in the paper. |
| Open Datasets | Yes | The dataset is available at http://ucc.insight-centre.org/bhurley/ |
| Dataset Splits | Yes | We use a standard randomised 10-fold cross-validation, where the dataset is split in 10 folds. |
| Hardware Specification | Yes | Performance data was collected on a cluster of Intel Xeon E5430 2.66Ghz processors running Cent OS 6. |
| Software Dependencies | Yes | We use Mini Sat 2.0 [E en and S orensson, 2004] as the solver... running Cent OS 6. |
| Experiment Setup | Yes | For the predictions in this section we will use the adjusted logtransformed runtime, log10(1 + runtime). ... We use a random forest with the same parameters as [Hutter et al., 2014], that is using 10 regression trees, using half of the variables as split variables at each node (perc = 0.5), a maximum of 5 data-points in leaf nodes (nmin = 5). |