[2017-06-23] Prof. Kai-Wei Chang, UCLA, "Structured Predictions: Practical Advancements and Applications in Natural Language Processing”


Title: Structured Predictions: Practical Advancements and Applications in Natural Language Processing
ocation: R104, CSIE
Speaker: Prof. Kai-Wei Chang, UCLA

Hosted by: Prof. Vivian Chen


Many machine learning problems involve making joint predictions over a set of mutually dependent output variables. The dependencies between output variables can be represented by a structure, such as a sequence, a tree, a clustering of nodes, or a graph. Structured prediction models have been proposed for problems of this type, and they have been successfully applied in many natural language processing applications. In this talk, I will describe a collection of results that improve several aspects of structured prediction approaches. Our results lead to efficient learning algorithms for structured prediction models, which, in turn, support reduction in problem size, improvements in training and evaluation speed, and improved performance. Related information is at http://www.cs.virginia.edu/~kc2wc/talk/sp.html


Kai-Wei Chang is an assistant professor in the Department of Computer Science at the University of California at Los Angeles. He has published broadly in machine learning and natural language processing. His research has mainly focused on designing machine learning methods for handling large and complex data. He has been involved in developing several machine learning libraries, including LIBLINEAR, Vowpal Wabbit, and Illinois-SL. He was an assistant professor at the University of Virginia in 2016-2017. He obtained his Ph.D. from the University of Illinois at Urbana-Champaign in 2015 and was a post-doctoral researcher at Microsoft Research in 2016. Kai-Wei was awarded the KDD Best Paper Award (2010), the Yahoo! Key Scientific Challenges Award (2011), and the C.L. and Jane W-S. Liu Award (2013). Additional information is available at http://kwchang.net.

最後修改時間:2017-06-23 AM 9:15

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