Feb. 9, 2024, 5:45 a.m. | Parker Knight Rui Duan

stat.ML updates on arXiv.org arxiv.org

Multi-task learning has emerged as a powerful machine learning paradigm for integrating data from multiple sources, leveraging similarities between tasks to improve overall model performance. However, the application of multi-task learning to real-world settings is hindered by data-sharing constraints, especially in healthcare settings. To address this challenge, we propose a flexible multi-task learning framework utilizing summary statistics from various sources. Additionally, we present an adaptive parameter selection approach based on a variant of Lepski's method, allowing for data-driven tuning parameter …

application challenge constraints data framework healthcare machine machine learning multiple multi-task learning paradigm performance statistics stat.me stat.ml summary tasks world

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