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后打击和早期疾病的特定疾病，从而导致许多症状和疾病。 一个基本的问题是，不同类型的事件如何相互关系，以及一种类型的事件如何引起另一种类型的事件。该项目的目的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，该方法使用依赖历史的强度函数对事件发生的瞬时可能性进行了建模。该项目将专门研究因果关系的概念，该概念提供了解决如何控制时间标记点过程的问题的一般框架。为了使方法实用，当点过程数据嘈杂且不完整时，该项目还将调查统计学习问题。这项研究针对健康信息学和电子商务。对于医疗保健，这项研究有可能在疾病进展中发现临床上有意义的合并症，并为了改善医疗保健结果而优化治疗方案。对于电子商务而言，该研究有可能提高公司的运营效率并提高用户体验。该项目将着重于机器学习和数据挖掘方法和用于建模，学习和控制时间标记点过程的算法。目的是理解和建模目前和未来的特定类型事件的发生如何取决于过去发生的相同事件和其他类型的事件的发生，以及这种动态依赖性如何在整个人群中以及整个时间内表现出异质性。最终，它将利用对时间标记点过程的动态特性的了解来设计干预策略，以改变其时间演变，从而实现更理想的结果。 该项目的技术核心是开发基于时间强度的时间标记点过程的基于强度的因果动态模型，其目标是将Granger因果关系扩展到时间点过程的背景下，以使时间上的明显点过程的算法更加实用，从系统地上进行算法，以在各种不完美的观察和控制范围内进行序列的序列，以实现各种序列，以在各种不完美的观察过程中进行序列，以控制时间范围，以控制时间效果，并控制时间范围的范围。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