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Invited Talk 11: Tensor Methods for Efficient and Interpretable Spatiotemporal Learning
Rose Yu

Fri Dec 11 04:10 PM -- 04:32 PM (PST) @

Multivariate spatiotemporal data is ubiquitous in science and engineering, from climate science to sports analytics, to neuroscience. Such data contain higher-order correlations and can be represented as a tensor. Tensor latent factor models provide a powerful tool for reducing dimensionality and discovering higher-order structures. However, existing tensor models are often slow or fail to yield interpretable latent factors. In this talk, I will demonstrate advances in tensor methods to generate interpretable latent factors for high-dimensional spatiotemporal data. We provide theoretical guarantees and demonstrate their applications to real-world climate, basketball, and neuroscience data.

Author Information

Rose Yu (University of California, San Diego)

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