操龍兵教授學術報告會

發布時間:2019-07-04

報告題目:Statistical Learning of Large-scale, Sparse and Multi-source data

報 告 人:操龍兵 教授

報告時間:201974日(周四)下午 14:30

報告地點:安徽大學磬苑校區理工D312會議室

Real-life big data is often very large, sparse, and multi-sourced. Such big data challenges widely appear in almost all big data applications including finance, communications, e-commerce, and social networks. Traditional statistical learning methods face significant problems because of the intensive mathematical computation and inference required. A recent direction in statistical learning is to develop efficient statistical methods to learn complex big data, requiring handling the bigness, high sparsity, heterogeneity and coupling in both observable and hidden spaces. Accordingly, this talk introduces some new statistical models for tackling such challenges on large, sparse and multi-source data with efficient Bayesian inference methods, in addition to its applications to large-scale and sparse recommendation and some new directions of statistically learning complex big data problems.

主辦單位:計算機科學與技術學院

歡迎各位老師、同學屆時前往!

科學技術處

  201974


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