Nonparametric Machine Learning for Modern Data Analytics

该项目旨在开发下一代机器学习方法，以科普不断增长的数据洪流。现代数据分析任务需要从复杂的、不断增长的数据中解释和获取价值。该项目的预期成果包括新的贝叶斯非参数方法，可以表达具有无限模型复杂性的多个异构数据源之间的任意依赖关系，以及执行推理和从中推导知识的算法;新的贝叶斯统计推理，用于集值随机变量，超越向量和矩阵，以丰富我们的分析工具箱来处理集合;并提出了一种新的确定性快速推理方法以满足实际需求。

This project intends to develop next-generation machine-learning methods to cope with the growing data deluge. Modern data analytics tasks need to interpret and derive values from complex, growing data. Intended outcomes of the project include new Bayesian nonparametric methods that can express arbitrary dependency amongst multiple, heterogeneous data sources with infinite model complexity, together with algorithms to perform inference and deduce knowledge from them; new Bayesian statistical inference for set-valued random variables that moves beyond vectors and matrices to enrich our analytics toolbox to deal with sets; and a new deterministic fast inference to meet with real-world demand.