Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling

NeurIPS 2019 Qitian WuZixuan ZhangXiaofeng GaoJunchi YanGuihai Chen

We target modeling latent dynamics in high-dimension marked event sequences without any prior knowledge about marker relations. Such problem has been rarely studied by previous works which would have fundamental difficulty to handle the arisen challenges: 1) the high-dimensional markers and unknown relation network among them pose intractable obstacles for modeling the latent dynamic process; 2) one observed event sequence may concurrently contain several different chains of interdependent events; 3) it is hard to well define the distance between two high-dimension event sequences... (read more)

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