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Thinning for Accelerating the Learning of Point Processes
Tianbo Li · Yiping Ke

Thu Dec 05:00 PM -- 07:00 PM PST @ East Exhibition Hall B + C #58

This paper discusses one of the most fundamental issues about point processes that what is the best sampling method for point processes. We propose \textit{thinning} as a downsampling method for accelerating the learning of point processes. We find that the thinning operation preserves the structure of intensity, and is able to estimate parameters with less time and without much loss of accuracy. Theoretical results including intensity, parameter and gradient estimation on a thinned history are presented for point processes with decouplable intensities. A stochastic optimization algorithm based on the thinned gradient is proposed. Experimental results on synthetic and real-world datasets validate the effectiveness of thinning in the tasks of parameter and gradient estimation, as well as stochastic optimization.

Author Information

Tianbo Li (Nanyang Technological University)
Yiping Ke (Nanyang Technological University)