CAREER: Embedding High-Order Interaction Events: Models, Algorithms, and Applications

职业：嵌入高阶交互事件：模型、算法和应用

• 批准号: 2046295
• 负责人: Shandian Zhe
• 金额: $ 54.93万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046295&HistoricalAwards=false
• 关键词: CAREER; Embedding; Order; Interaction; Events

High-order interaction events of multiple entities are ubiquitous, ranging from online advertising to commodity recommendation, from neural-signal transduction to gene regulation to disease spreading to international affairs. For example, online shopping behaviors are interaction events between customers, products and selling platforms. This project develops flexible, interpretable, and scalable Bayesian embeddings for massive high-order interaction events, in order to understand a variety of complex relationships between the events and discover the underlying rich patterns. The developed tools can fundamentally promote many important knowledge mining and prediction tasks. Examples include predicting the occurrence of hazardous online transactions to enhance financial security, predicting the outbreak and spreading of pandemic diseases to take effective preventive actions, early warnings of catastrophes, studying when and how rumors propagate through online social media, etc. Current approaches for event data analysis are mostly restricted to binary interactions, and suffer from rough, over-simplified or opaque, uninterpretable modeling with limited computational efficiency. The goal of the project is to develop Bayesian embeddings that can efficiently process tremendous batch and fast streaming event data, capture both the static relationships of the entities and a variety of short-term, long-term, triggering, inhibition, and time varying influences among the events, and encode all of these into embedding representations to uncover rich temporal patterns. The research will be accomplished through four primary tasks: (1) using marked point processes to design highly expressive yet transparent Bayesian embedding models, (2) using variational transforms and composite Monte-Carlo approximations to fulfill stochastic mini-batch gradient and asynchronous stochastic learning on extremely large-scale batch data, (3) efficient posterior incremental learning for rapid event streams, and (4) comprehensive evaluations on synthetic and real-world applications. Moreover, using Bayesian frameworks, the developed tools are resilient to noises, provide posterior distributions to quantify uncertainties, and integrate all possible outcomes into robust predictions. The contribution is expected to dramatically promote the use of embedding as a means of temporal knowledge mining and predictive analytics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

从网络广告到商品推荐，从神经信号传导到基因调控，从疾病传播到国际事务，多个实体的高阶交互事件无处不在。例如，网络购物行为是客户、产品和销售平台之间的交互事件。该项目为大量高阶交互事件开发灵活，可解释和可扩展的贝叶斯嵌入，以了解事件之间的各种复杂关系并发现潜在的丰富模式。所开发的工具可以从根本上促进许多重要的知识挖掘和预测任务。例如，预测危险在线交易的发生以增强金融安全，预测流行性疾病的爆发和传播以采取有效的预防措施，对灾难进行早期预警，研究谣言何时以及如何通过在线社交媒体传播等。不可解释的建模，计算效率有限。该项目的目标是开发贝叶斯嵌入，可以有效地处理大量的批量和快速流事件数据，捕获实体的静态关系以及事件之间的各种短期，长期，触发，抑制和随时间变化的影响，并将所有这些编码为嵌入表示，以揭示丰富的时间模式。这项研究将通过四项主要任务来完成：（1）使用标记点过程设计高表达性且透明的贝叶斯嵌入模型，（2）使用变分变换和复合蒙特-卡罗近似实现随机小批量梯度和超大规模批量数据的异步随机学习，（3）快速事件流的有效后验增量学习，（4）综合评价合成和实际应用。此外，使用贝叶斯框架，所开发的工具是弹性的噪声，提供后验分布量化的不确定性，并集成到强大的预测所有可能的结果。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Nonparametric Embeddings of Sparse High-Order Interaction Events

• DOI: 10.48550/arxiv.2207.03639
• 发表时间: 2022-07
• 作者: Z. Wang;Yiming Xu;Conor Tillinghast;Shibo Li;A. Narayan;Shandian Zhe

Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor

连续索引张量的函数贝叶斯塔克分解

• 发表时间: 2024
• 期刊: Proceedings of The International Conference on Learning Representations (ICLR
• 作者: Fang, Shikai;Yu, Xin;Wang, Zheng;Li, Shibo;Kirby, Robert M.;Zhe, Shandian
• 通讯作者: Zhe, Shandian

Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels

• DOI: 10.48550/arxiv.2310.05387
• 发表时间: 2023-10
• 作者: Da Long;Wei W. Xing;Aditi S. Krishnapriyan;R. Kirby;Shandian Zhe;Michael W. Mahoney

Decomposing Temporal High-Order Interactions via Latent ODEs

• 发表时间: 2022
• 作者: Shibo Li;Robert M. Kirby;Shandian Zhe

Self-Adaptable Point Processes with Nonparametric Time Decays

具有非参数时间衰减的自适应点过程

• 发表时间: 2021
• 期刊: Proceedings of Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS
• 作者: Pan, Zhimeng;Wang, Zheng;Phillips, Jeff;Zhe, Shandian
• 通讯作者: Zhe, Shandian