BandiK: Efficient Multi-Task Learning with Multi-Bandits
Analysis
Key Takeaways
- •Proposes BandiK, a novel three-stage multi-task auxiliary task subset selection method.
- •Utilizes a multi-bandit framework to efficiently evaluate candidate auxiliary task sets.
- •Addresses the computational and combinatorial challenges of multi-task learning.
- •Aims to improve knowledge transfer and downstream task performance.
“BandiK employs a Multi-Armed Bandit (MAB) framework for each task, where the arms correspond to the performance of candidate auxiliary sets realized as multiple output neural networks over train-test data set splits.”