Abstract

Multi-task learning requires accurate identification of the correlations between tasks. In real-world time-series, tasks are rarely perfectly temporally aligned; traditional multitask models do not account for this and subsequent errors in correlation estimation will result in poor predictive performance and uncertainty quantification. We introduce a method that automatically accounts for temporal misalignment in a unified generative model that improves predictive performance. Our method uses Gaussian processes (GPs) to model the correlations both within and between the tasks. Building on the previous work by Kazlauskaite et al. (2019), we include a separate monotonic warp of the input data to model temporal misalignment. In contrast to previous work, we formulate a lower bound that accounts for uncertainty in both the estimates of the warping process and the underlying functions. Also, our new take on a monotonic stochastic process, with efficient path-wise sampling for the warp functions, allows us to perform full Bayesian inference in the model rather than MAP estimates. Missing data experiments, on synthetic and real time-series, demonstrate the advantages of accounting for misalignments (vs standard unaligned method) as well as modelling the uncertainty in the warping process (vs baseline MAP alignment approach).

Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS)
EditorsGustau Camps-Valls, Francisco J. R. Ruiz, Isabel Valera
Pages2970-2988
Number of pages19
Volume151
Publication statusPublished - 30 Mar 2022
Event25th International Conference on Artificial Intelligence and Statistics, AISTATS 2022 - Virtual, Online, Spain
Duration: 28 Mar 202230 Mar 2022

Publication series

NameProceedings of Machine Learning Research
ISSN (Print)2640-3498

Conference

Conference25th International Conference on Artificial Intelligence and Statistics, AISTATS 2022
Country/TerritorySpain
CityVirtual, Online
Period28/03/2230/03/22

Bibliographical note

Funding Information:
This work was supported by the Swedish Foundation for Strategic Research, Grant No. RIT15-0107 (EACare), EPSRC grant EP/P020720/2 for Inference, COmputation and Numerics for Insights into Cities (ICONIC, https://iconicmath.org/), the UKRI CAMERA project (EP/T022523/1) and the Royal Society.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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