2024-04-18-augusto-zagatti24a.md

title	software	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Learning multivariate temporal point processes via the time-change theorem	https://github.com/NUS-IDS/multi-ttpp	Marked temporal point processes (TPPs) are a class of stochastic processes that describe the occurrence of a countable number of marked events over continuous time. In machine learning, the most common representation of marked TPPs is the univariate TPP coupled with a conditional mark distribution. Alternatively, we can represent marked TPPs as a multivariate temporal point process in which we model each sequence of marks interdependently. We introduce a learning framework for multivariate TPPs leveraging recent progress on learning univariate TPPs via time-change theorems to propose a deep-learning, invertible model for the conditional intensity. We rely neither on Monte Carlo approximation for the compensator nor on thinning for sampling. Therefore, we have a generative model that can efficiently sample the next event given a history of past events. Our models show strong alignment between the percentiles of the distribution expected from theory and the empirical ones.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	augusto-zagatti24a	0	Learning multivariate temporal point processes via the time-change theorem	3241	3249	3241-3249	3241	false	Augusto Zagatti, Guilherme and Kiong Ng, See and Bressan, St\'{e}phane	given family Guilherme Augusto Zagatti given family See Kiong Ng given family Stéphane Bressan	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/augusto-zagatti24a/augusto-zagatti24a.pdf