PhD Candidate
Georgia Institute of Technology
Areas of Interest
- Artificial Intelligence
Poster
Meta Learning with Relational Information for Short Sequences
Abstract
This work proposes a new meta-learning method – named HARMLESS (HAwkes Relational Meta LEarning method for Short Sequences) for learning heterogeneous point process models from 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 adaptive learning for each individual sequence. We further propose an efficient stochastic variational meta expectation maximization algorithm that can scale to large problems. Numerical experiments on both synthetic and real data show that HARMLESS outperforms existing methods in terms of predicting future events.
Bio
PhD, Computational Sceince and Engineering, Georgia Institute of Technology, U.S.A. (2016 - Present)
BS, Theoretic and Applied Mechanics, University of Science and Technology of China, China (2012 - 2016)
