Algorithm selection aims to identify the most suitable algorithm for solving a specific problem
before execution, which has become a critical process of the AutoML. Current mainstream …

Abstract Existing Automated machine learning (AutoML) systems have achieved
considerable success in offline machine learning. Nonetheless, they are not applicable to …

We consider a resource-aware variant of the classical multi-armed bandit problem: In each
round, the learner selects an arm and determines a resource limit. It then observes a …

Online algorithm selection concerns the task of designing a dynamic algorithm selector that
observes sequentially arriving problem instances of an algorithmic problem class for which it …

Abstract Accelerated Failure Time (AFT) models are popular models used in survival
analysis. AFT models are also an important alternative to the Cox Proportional Hazards (PH) …

Realtime algorithm configuration is concerned with the task of designing a dynamic
algorithm configurator that observes sequentially arriving problem instances of an …