Bayesian learning from sequential data using Gaussian processes with signature covariances
We develop a Bayesian approach to learning from sequential data by using Gaussian processes (GPs) with so-called signature kernels as covariance functions. This allows to make sequences of different length comparable and to rely on strong theoretical results from stochastic analysis. Signatures capt...
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| Format: | Conference item |
| Language: | English |
| Published: |
Proceedings of Machine Learning Research
2020
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