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Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization
Leona Hennig, Jasmin Brandt, Lukas Fehring +3
Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing concerns about scalability and en…
Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
Lukas Fehring, Marcel Wever, Maximilian Spliethöver +3
Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO). However, most existing HPO methods only incorporate expert knowledge during initializatio…
Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning
Lukas Fehring, Marius Lindauer, Theresa Eimer
While increasingly large models have revolutionized much of the machine learning landscape, training even mid-sized networks for Reinforcement Learning (RL) is still proving to be…
HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection
Lukas Fehring, Jonas Hanselle, Alexander Tornede
It is well known that different algorithms perform differently well on an instance of an algorithmic problem, motivating algorithm selection (AS): Given an instance of an algorithm…