As-llm: When algorithm selection meets large language model
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 …
before execution, which has become a critical process of the AutoML. Current mainstream …
Multi-armed bandits with censored consumption of resources
V Bengs, E Hüllermeier - Machine Learning, 2023 - Springer
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 …
round, the learner selects an arm and determines a resource limit. It then observes a …
Online Automated Imbalanced Learning via Adaptive Thompson Sampling
Z Wang, S Wang - International Conference on Pattern Recognition, 2024 - Springer
Abstract Existing Automated machine learning (AutoML) systems have achieved
considerable success in offline machine learning. Nonetheless, they are not applicable to …
considerable success in offline machine learning. Nonetheless, they are not applicable to …
[PDF][PDF] Contextual Preselection Methods in Pool-based Realtime Algorithm Configuration.
Realtime algorithm configuration is concerned with the task of designing a dynamic
algorithm configurator that observes sequentially arriving problem instances of an …
algorithm configurator that observes sequentially arriving problem instances of an …
[PDF][PDF] Minimum Hellinger Distance Estimation of AFT Models with Right-Censored Data
Y Huang - 2024 - prism.ucalgary.ca
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) …
analysis. AFT models are also an important alternative to the Cox Proportional Hazards (PH) …