When to Transfer: Adaptive Source Selection for Positive Transfer in Linear Models
Researchers have developed a new method for transfer learning in linear models, focusing on scenarios where labeled data for a target task is limited. The approach adaptively selects which source datasets to transfer from and how many samples to use, employing an accept/reject rule based on estimated transfer gain. This method aims to maximize positive transfer and minimize negative transfer, demonstrating consistent gains over existing baselines in experiments with both synthetic and real-world data. AI
IMPACT Introduces a novel statistical technique for optimizing data transfer in machine learning, potentially improving model performance in data-scarce environments.