【15-33期VALSE Webinar活动】 报告嘉宾：谢澎涛（CMU ML Dept）
报告时间：2015年10月21日（星期三）晚20:00（北京时间）
报告题目：Diversity Regularization of Latent Variable Models: Theory, Algorithm and Applications [Slides]
主持人：禹之鼎（CMU ECE Dept）
报告摘要：Latent Variable Models (LVMs) or latent space models are a large family of machine learning models that have been widely utilized in computer vision, text mining, computational biology, to name a few. In this talk, I will introduce a new type of regularization approach of LVMs: diversity regularization, which encourages the components in LVMs to be diverse, with the aim to (1) capture long tail factors in knowledge; (2) reduce model complexity without sacrificing expressiveness. Specifically, I will introduce the motivation of designing this regularizer, how it is formally defined, how to optimize it, its theoretical analysis and empirically applications.
参考文献：
[1] Pengtao Xie, Yuntian Deng, and Eric Xing. Diversifying Restricted Boltzmann Machine for Document Modeling. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (KDD 2015)
[2] Pengtao Xie. Diversifying Distance Metric Learning. In European Conference on Machine Learning. (ECML 2015)
报告人简介：Pengtao Xie is a PhD student in the Machine Learning Department at Carnegie Mellon University. His primary research focus is latent variable models, in particular developing geometric regularization approaches to reduce model complexity without compromising expressiveness and building distributed systems to facilitate large scale latent variable modeling. He received a M.E. from Tsinghua University in 2013 and a B.E. from Sichuan University in 2010. He is the recipient of Siebel Scholarship, Goldman Sachs Global Leader Scholarship and National Scholarship of China.
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