DAC is capable of generalizing over prior optimization approaches, as well as handling optimization of hyperparameters that need to be adjusted over multiple time-steps.
DACBench is a benchmark library for Dynamic Algorithm Configuration. Its focus is on reproducibility and comparability of different DAC methods as well as easy analysis of the optimization process.
CARL is a benchmark for contextual RL (cRL). In cRL, we aim to generalize over different contexts. In CARL we saw that if we vary the context, the learning becomes more difficult, and making the context explicit can facilitate learning.
CAVE stands for Configuration Visualization, Assessment and Evaluation. It is a versatile analysis tool for automatic algorithm configurators.
PyImp is an easy to use tool that helps developers to identify the most important parameters of their algorithms. Given the data of a configuration run with SMAC3, PyImp allows one to use Forward Selection, Efficient Ablation and Influence Models to determine which Parameters have the most influence over the algorithms behaviour.
DeepCAVE, the successor of CAVE, is an interactive framework to visualize and analyze AutoML processes in real-time.
SMAC is a tool for algorithm configuration to optimize the parameters of arbitrary algorithms, including hyperparameter optimization of Machine Learning algorithms. The main core consists of Bayesian Optimization in combination with an aggressive racing mechanism to efficiently decide which of two configurations performs better.