Discovering Nonlinear Relations with Minimum Predictive Information Regularization

• URL: http://arxiv.org/abs/2001.01885v1
• Date: Tue, 7 Jan 2020 04:28:00 GMT
• Title: Discovering Nonlinear Relations with Minimum Predictive Information Regularization
• Authors: Tailin Wu, Thomas Breuel, Michael Skuhersky and Jan Kautz
• Abstract summary: We introduce a novel minimum predictive information regularization method to infer directional relations from time series. Our method substantially outperforms other methods for learning nonlinear relations in synthetic datasets.
• Score: 67.7764810514585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying the underlying directional relations from observational time series with nonlinear interactions and complex relational structures is key to a wide range of applications, yet remains a hard problem. In this work, we introduce a novel minimum predictive information regularization method to infer directional relations from time series, allowing deep learning models to discover nonlinear relations. Our method substantially outperforms other methods for learning nonlinear relations in synthetic datasets, and discovers the directional relations in a video game environment and a heart-rate vs. breath-rate dataset.

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