Efficient benchmarking of algorithm configurators via model-based surrogates
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Publication:1707462
DOI10.1007/s10994-017-5683-zzbMath1457.68341OpenAlexW2604867865MaRDI QIDQ1707462
Marius Lindauer, Kevin Leyton-Brown, Holger H. Hoos, Frank Hutter, Katharina Eggensperger
Publication date: 3 April 2018
Published in: Machine Learning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10994-017-5683-z
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Cites Work
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- MiniSat
- HPOlib
- ASlib: a benchmark library for algorithm selection
- Algorithm runtime prediction: methods \& evaluation
- The configurable SAT solver challenge (CSSC)
- A meta-learning method to select the kernel width in support vector regression
- The design and analysis of computer experiments.
- Design and analysis of computer experiments. With comments and a rejoinder by the authors
- Conflict-driven answer set solving: from theory to practice
- Support-vector networks
- Bayesian learning for neural networks
- SpyBug: Automated Bug Detection in the Configuration Space of SAT Solvers
- Choosing Probability Distributions for Stochastic Local Search and the Role of Make versus Break
- Parsing Combinatory Categorial Grammar via Planning in Answer Set Programming
- claspfolio2: Advances in Algorithm Selection for Answer Set Programming
- Empirical hardness models
- Metalearning
- ParamILS: An Automatic Algorithm Configuration Framework
- A Parallel Mixture of SVMs for Very Large Scale Problems
- Automated Testing and Debugging of SAT and QBF Solvers
- Automatic model selection for high-dimensional survival analysis
- Theory and Applications of Satisfiability Testing
- Principles and Practice of Constraint Programming – CP 2004
- Random forests
