III: Small: Topics in Temporal Marked Point Processes: Granger Causality, Imperfect Observations and Intervention

III：小：时间标记点过程的主题：格兰杰因果关系、不完美观察和干预

• 批准号: 1717916
• 负责人: Tuo Zhao
• 金额: $ 45万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717916&HistoricalAwards=false
• 关键词: III; Small; Topics; Temporal; Marked

Event sequences are ubiquitous in many important applications. For example, a major earthquake may trigger many after-shocks and a specific disease in early life leads to many symptoms and diseases later on. A fundamental question is how different types of events relate to each other and how one type of event causes the occurrence of another type of event. The objective of this project is to address this question by leveraging the probabilistic and statistical methodology of temporal marked point processes, which models the instantaneous likelihood of an event occurrence using history dependent intensity functions. The project will specifically investigate the notion of causality that provides a general framework for tackling the problem of how to control a temporal marked point process. To make the methods practical, the project will also investigate statistical learning problems when point process data are noisy and incomplete. This research targets health informatics and e-commerce. For healthcare, the research has the potential to uncover clinically meaningful comorbidity in disease progression as well as to optimize treatment regimes for the purpose of improving healthcare outcomes. For e-commerce, the research has the potential to improve companies' operational efficiency and enhance user experiences.This project will focus on machine learning and data mining methodology and algorithms for modeling, learning and control of temporal marked point processes. The goal is in understanding and modeling of how the occurrences of a specific type of events at present and future depend on the occurrences of events of the same and other types happened in the past, and how this dynamic dependency exhibits heterogeneity across a population and across time. Ultimately, it will leverage knowledge of the dynamic properties of temporal marked point processes to design intervention policies to change their time evolution so as to achieve more desirable outcomes. The technical core of the project is to develop intensity based causal dynamic models of temporal marked point processes with the goal to extend Granger causality to the context of temporal point processes, to make algorithms for temporal marked point processes more practical by systematically investigating inference algorithms under a variety of imperfect observations and to develop methods for the manipulation and control of the time evolution of temporal marked point processes in order to achieve more desirable outcomes.

事件序列在许多重要应用中无处不在。例如，一场大地震可能会引发许多余震，而早年的某种特定疾病会导致后来的许多症状和疾病。 一个基本问题是不同类型的事件如何相互关联以及一种类型的事件如何导致另一种类型的事件的发生。该项目的目标是通过利用时间标记点过程的概率和统计方法来解决这个问题，该方法使用历史相关强度函数对事件发生的瞬时可能性进行建模。该项目将专门研究因果关系的概念，为解决如何控制时间标记点过程的问题提供一个通用框架。为了使这些方法实用，该项目还将研究当点过程数据有噪声且不完整时的统计学习问题。这项研究针对健康信息学和电子商务。对于医疗保健而言，该研究有可能发现疾病进展中具有临床意义的合并症，并优化治疗方案以改善医疗保健结果。对于电子商务，该研究有潜力提高公司的运营效率并增强用户体验。该项目将重点关注机器学习和数据挖掘方法以及用于对时间标记点过程进行建模、学习和控制的算法。目标是理解和建模当前和未来特定类型事件的发生如何取决于过去发生的相同和其他类型事件的发生，以及这种动态依赖性如何在人群和时间上表现出异质性。最终，它将利用时间标记点过程的动态特性的知识来设计干预策略来改变其时间演化，从而实现更理想的结果。 该项目的技术核心是开发基于强度的时间标记点过程因果动态模型，目标是将格兰杰因果关系扩展到时间点过程的背景下，通过系统地研究各种不完美观测下的推理算法，使时间标记点过程的算法更加实用，并开发操纵和控制时间标记点过程的时间演化的方法，以实现更多 理想的结果。

项目成果

On Computation and Generalization of Generative Adversarial Imitation Learning

• 发表时间: 2020-01
• 期刊: ArXiv
• 作者: Minshuo Chen;Yizhou Wang;Tianyi Liu;Zhuoran Yang;Xingguo Li;Zhaoran Wang;T. Zhao

CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning

• 发表时间: 2018
• 作者: Jiachen Yang;A. Nakhaei;David Isele;Hongyuan Zha;K. Fujimura

On Generalization Bounds of a Family of Recurrent Neural Networks

• 发表时间: 2018-09
• 作者: Minshuo Chen;Xingguo Li;T. Zhao

Implicit Bias of Gradient Descent based Adversarial Training on Separable Data

• 发表时间: 2020-04
• 作者: Yan Li;Ethan X. Fang;Huan Xu;T. Zhao

Learning to Defend by Learning to Attack

• 发表时间: 2018-11
• 作者: Haoming Jiang;Zhehui Chen;Yuyang Shi;Bo Dai;T. Zhao