1 code implementation • 25 Oct 2023 • Zichong Li, Yanbo Xu, Simiao Zuo, Haoming Jiang, Chao Zhang, Tuo Zhao, Hongyuan Zha
We conduct extensive experiments in both event type prediction and uncertainty quantification of arrival time.
no code implementations • 25 Oct 2023 • Zichong Li, Qunzhi Xu, Zhenghao Xu, Yajun Mei, Tuo Zhao, Hongyuan Zha
Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of marked STPPs through score-matching and offers uncertainty quantification for the predicted event time, location and mark by computing confidence regions over the generated samples.
no code implementations • 19 Dec 2022 • Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu
In this paper, we design and analyze stochastic inexact augmented Lagrangian methods (Stoc-iALM) to solve problems involving a nonconvex composite (i. e. smooth+nonsmooth) objective and nonconvex smooth functional constraints.
3 code implementations • ICML 2020 • Simiao Zuo, Haoming Jiang, Zichong Li, Tuo Zhao, Hongyuan Zha
Modern data acquisition routinely produce massive amounts of event sequence data in various domains, such as social media, healthcare, and financial markets.