文章基本信息

标题：Learning stable and predictive structures in kinetic systems
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作者：Niklas Pfister ; Stefan Bauer ; Jonas Peters 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2019
卷号：116
期号：51
页码：25405-25411
DOI：10.1073/pnas.1905688116
出版社：The National Academy of Sciences of the United States of America
摘要：Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework, called CausalKinetiX, that identifies structure from discrete time, noisy observations, generated from heterogeneous experiments. The algorithm assumes the existence of an underlying, invariant kinetic model, a key criterion for reproducible research. Results on both simulated and real-world examples suggest that learning the structure of kinetic systems benefits from a causal perspective. The identified variables and models allow for a concise description of the dynamics across multiple experimental settings and can be used for prediction in unseen experiments. We observe significant improvements compared to well-established approaches focusing solely on predictive performance, especially for out-of-sample generalization..
关键词：kinetic systems ; causal inference ; stability ; invariance ; structure learning