Multi-task Dynamical Systems

Chris Williams (University of Edinburgh)

Friday 12th March 15:00-16:00 ZOOM:


Time series datasets are often composed of a variety of sequences from
the same domain, but from different entities, such as individuals,
products, or organizations. We are interested in how time series
models can be specialized to individual sequences (capturing the
specific characteristics) while still retaining statistical power by
sharing commonalities across the sequences. We describe the
multi-task dynamical system (MTDS)---a general methodology for
extending multi-task learning (MTL) to time series models. Our
approach endows dynamical systems with a set of hierarchical latent
variables which can modulate all model parameters. To our knowledge,
this is a novel development of MTL, and applies to time series both
with and without control inputs. We apply the MTDS to motion-capture
data of people walking in various styles using a multi-task recurrent
neural network (RNN), and to patient drug-response data using a
multi-task pharmacodynamic model.

Joint work with Alex Bird and Chris Hawthorne

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