Algorithm Selection via Ranking
Authors: Richard Oentaryo, Stephanus Daniel Handoko, Hoong Chuin Lau
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the SAT 2012 competition dataset show that our approach yields competitive performance to that of more sophisticated algorithm selection methods. We evaluate the efficacy of our RAS approach through extensive experiments on the SAT 2012 competition data. |
| Researcher Affiliation | Academia | Living Analytics Research Centre, School of Information Systems Singapore Management University, Singapore 178902 |
| Pseudocode | Yes | Algorithm 1 SGD Procedure for Ranking Optimization |
| Open Source Code | No | The paper does not explicitly state that its code is open-source or provide a link for it. |
| Open Datasets | Yes | For our experiments, we use the SAT 2012 datasets supplied by the UBC group1, after SATZilla won the SAT 2012 Challenge. 1http://www.cs.ubc.ca/labs/beta/Projects/ SATzilla |
| Dataset Splits | Yes | As our evaluation procedure, we adopt 10-fold cross validation. Specifically, we partition the problem instances (i.e., rows of the matrix) into 10 equal parts, and generate 10 pairs of training and testing data. For each fold, we enforce that 10% of the instances contained in the testing data do not appear in the training data. |
| Hardware Specification | No | The paper does not specify the hardware used for running experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in their implementation or experiments. |
| Experiment Setup | Yes | We set the parameters of our RAS method as follows: the learning rate η = 10-2, regularization parameter λ = 10-4, and maximum iterations Tmax = 25. For the RF method, we set the number of trees to 99 as per (Xu et al. 2012), and configured it to be as close as possible to SATZilla. |