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Poster
Meta Learning with Relational Information for Short Sequences
Yujia Xie · Haoming Jiang · Feng Liu · Tuo Zhao · Hongyuan Zha

Tue Dec 10 05:30 PM -- 07:30 PM (PST) @ East Exhibition Hall B + C #44
in Algorithms -- Meta-Learning »

This paper proposes a new meta-learning method -- named HARMLESS (HAwkes Relational Meta Learning method for Short Sequences) for learning heterogeneous point process models from a collection of short event sequence data along with a relational network. Specifically, we propose a hierarchical Bayesian mixture Hawkes process model, which naturally incorporates the relational information among sequences into point process modeling. Compared with existing methods, our model can capture the underlying mixed-community patterns of the relational network, which simultaneously encourages knowledge sharing among sequences and facilitates adaptively learning for each individual sequence. We further propose an efficient stochastic variational meta-EM algorithm, which can scale to large problems. Numerical experiments on both synthetic and real data show that HARMLESS outperforms existing methods in terms of predicting the future events.

Keywords: Time Series Analysis · Applications · Algorithms -> Meta-Learning; Algorithms -> Unsupervised Learning; Applications -> Computational Social Science; Applications
[ Paper]  [ Poster] 

Author Information

Yujia Xie (Georgia Institute of Technology)
Haoming Jiang (Georgia Institute of Technology)
Feng Liu (Florida Atlantic University)
Tuo Zhao (Georgia Tech)
Hongyuan Zha (Georgia Tech)

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