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NL Seminar-Structured Predictions: Practical Advancements and Applications in Natural Language Processing
Fri, Nov 03, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Kai-Wei Chang , UCLA
Talk Title: Structured Predictions: Practical Advancements and Applications in Natural Language Processing
Series: Natural Language Seminar
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
Biography: 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.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://kwchang.net
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://kwchang.net
