6 citations · 27 across the 10 of their papers we have counts for
6 papers · 1 filter
Automated Dynamic Algorithm Configuration
Steven Adriaensen, André Biedenkapp, Gresa Shala +4
The performance of an algorithm often critically depends on its parameter configuration. While a variety of automated algorithm configuration methods have been proposed to relieve…
Neural Model-based Optimization with Right-Censored Observations
Katharina Eggensperger, Kai Haase, Philipp Müller +2
In many fields of study, we only observe lower bounds on the true response value of some experiments. When fitting a regression model to predict the distribution of the outcomes, w…
Learning Heuristic Selection with Dynamic Algorithm Configuration
David Speck, André Biedenkapp, Frank Hutter +2
A key challenge in satisficing planning is to use multiple heuristics within one heuristic search. An aggregation of multiple heuristic estimates, for example by taking the maximum…
The Algorithm Selection Competitions 2015 and 2017
Marius Lindauer, Jan N. van Rijn, Lars Kotthoff
The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance. It leverages the complementarity between different approaches that is…
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…
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…