Corpus ID: 202541246

Meta Learning with Relational Information for Short Sequences

@inproceedings{Xie2019MetaLW,
  title={Meta Learning with Relational Information for Short Sequences},
  author={Yujia Xie and Haoming Jiang and F. Liu and Tuo Zhao and H. Zha},
  booktitle={NeurIPS},
  year={2019}
}
  • Yujia Xie, Haoming Jiang, +2 authors H. Zha
  • Published in NeurIPS 2019
  • Mathematics, Computer Science
  • 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… CONTINUE READING
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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 59 REFERENCES
    Amortized Bayesian Meta-Learning
    30
    Probabilistic Model-Agnostic Meta-Learning
    169
    Meta Networks
    274
    Mixed Membership Stochastic Blockmodels
    1631
    A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering
    29
    Multi-Task Multi-Dimensional Hawkes Processes for Modeling Event Sequences
    39
    On First-Order Meta-Learning Algorithms
    408
    Discovering Latent Network Structure in Point Process Data
    186
    Dyadic event attribution in social networks with mixtures of hawkes processes
    28