881 citations · 1.2k across the 14 of their papers we have counts for
5 papers · 1 filter
Combining Automated Optimisation of Hyperparameters and Reward Shape
Julian Dierkes, Emma Cramer, Holger H. Hoos +1
There has been significant progress in deep reinforcement learning (RL) in recent years. Nevertheless, finding suitable hyperparameter configurations and reward functions remains c…
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML
Lennart Purucker, Lennart Schneider, Marie Anastacio +3
Automated machine learning (AutoML) systems commonly ensemble models post hoc to improve predictive performance, typically via greedy ensemble selection (GES). However, we believe…
Frugal Machine Learning
Mikhail Evchenko, Joaquin Vanschoren, Holger H. Hoos +2
Machine learning, already at the core of increasingly many systems and applications, is set to become even more ubiquitous with the rapid rise of wearable devices and the Internet…
Automated Algorithm Selection: Survey and Perspectives
Pascal Kerschke, Holger H. Hoos, Frank Neumann +1
It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is pa…
Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms
Chris Thornton, Frank Hutter, Holger H. Hoos +1
Many different machine learning algorithms exist; taking into account each algorithm's hyperparameters, there is a staggeringly large number of possible alternatives overall. We co…