Semi-supervised Meta-learning for Multi-source Heterogeneity in Time-series Data - researchr publication authors

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Lida Zhang, Bobak J. Mortazavi. Semi-supervised Meta-learning for Multi-source Heterogeneity in Time-series Data. In Kaivalya Deshpande, Madalina Fiterau, Shalmali Joshi, Zachary C. Lipton, Rajesh Ranganath, Iñigo Urteaga, Serene Yeung, editors, Machine Learning for Healthcare Conference, MLHC 2023, 11-12 August 2023, New York, USA. Volume 219 of Proceedings of Machine Learning Research, pages 923-941, PMLR, 2023. [doi]

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