Bayesian learning from sequential data using Gaussian processes with signature covariances

Conference item

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 capture sequential structure with tensors that can scale unfavourably in sequence length and state space dimension. To deal with this, we introduce a sparse variational approach with inducing tensors. We then combine the resulting GP with LSTMs and GRUs to build larger models that leverage the strengths of each of these approaches and benchmark the resulting GPs on multivariate time series (TS) classification datasets.

Authors: Oberhauser, H; Toth, C

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Proceedings of Machine Learning Research
• Journal: Proceedings of Machine Learning Research
• Volume: 119
• Pages: 9548-9560
• Publication date: 2020-11-21
• Acceptance date: 2020-06-01
• Event title: 37th International Conference of Machine Learning (ICML 2020)
• Event location: Vienna, Austria
• Event website: https://icml.cc/Conferences/2020
• Event start date: 2020-07-12
• Event end date: 2020-07-18
• Language: English
• Keywords: FFR