91 citations · 301 across the 27 of their papers we have counts for
5 papers · 1 filter
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning
René Sass, Eddie Bergman, André Biedenkapp +2
Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pip…
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…
Contextualize Me -- The Case for Context in Reinforcement Learning
Carolin Benjamins, Theresa Eimer, Frederik Schubert +6
While Reinforcement Learning ( RL) has made great strides towards solving increasingly complicated problems, many algorithms are still brittle to even slight environmental changes.…
Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration
André Biedenkapp, Nguyen Dang, Martin S. Krejca +2
It has long been observed that the performance of evolutionary algorithms and other randomized search heuristics can benefit from a non-static choice of the parameters that steer t…
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
Jack Parker-Holder, Raghu Rajan, Xingyou Song +9
The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable a…