Abstract
Graphical Event Models (GEMs) can approximate any smooth multivariate temporal point processes and can be used for capturing the dynamics of events occurring in continuous time for applications with event logs like web logs or gene expression data. In this paper, we propose a multi-task transfer learning algorithm for Timescale GEMs (TGEMs): the aim is to learn the set of k models given k corresponding datasets from k distinct but related tasks. The goal of our algorithm is to find the set of models with the maximal posterior probability. The procedure encourages the learned structures to become similar and simultaneously modifies the structures in order to avoid local minima. Our algorithm is inspired from an universal consistent algorithm for TGEM learning that retrieves both qualitative and quantitative dependencies from event logs. We show on a toy example that our algorithm could help to learn related tasks even with limited data.
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Notes
- 1.
It is another way of representing timescales. \(\mathcal {T} = (0,a], (a,b], (b,c]\) is equivalent to \(v = [0, a, b, c]\).
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Monvoisin, M., Leray, P. (2019). Multi-task Transfer Learning for Timescale Graphical Event Models. In: Kern-Isberner, G., Ognjanović, Z. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2019. Lecture Notes in Computer Science(), vol 11726. Springer, Cham. https://doi.org/10.1007/978-3-030-29765-7_26
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