Structured Predictions: Practical Advancements and Applications in Natural Language Processing

When:
Friday, November 3, 2017, 3:00 pm - 4:00 pm PDTiCal
Where:
11th Flr Conf Room-CR #1135
This event is open to the public.
Type:
NL Seminar
Speaker:
Kai-Wei Chang (UCLA)
Description:

Abstract: 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. In this talk, I will describe a collection of results that improve several aspects of these approaches. Our results lead to efficient and effective algorithms for learning structured prediction models, which, in turn, support weak supervision signals and improve training and evaluation speed. I will also discuss potential risks and challenges when using structured prediction models

Bio: Kai-Wei Chang is an assistant professor in the Department of Computer Science at the University of California, 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 EMNLP Best Long Paper Award (2017), KDD Best Paper Award (2010), and the Yahoo! Key Scientific Challenges Award (2011). Additional information is available at http://kwchang.net.

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