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Neural Networks for Predicting Algorithm Runtime Distributions
Katharina Eggensperger, Marius Lindauer, Frank Hutter
Many state-of-the-art algorithms for solving hard combinatorial problems in artificial intelligence (AI) include elements of stochasticity that lead to high variations in runtime,…
Warmstarting of Model-based Algorithm Configuration
Marius Lindauer, Frank Hutter
The performance of many hard combinatorial problem solvers depends strongly on their parameter settings, and since manual parameter tuning is both tedious and suboptimal the AI com…
Pitfalls and Best Practices in Algorithm Configuration
Katharina Eggensperger, Marius Lindauer, Frank Hutter
Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling…
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates
Katharina Eggensperger, Marius Lindauer, Holger H. Hoos +2
The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard com…