Computer Science

Computer Science Seminar: Dr. Kai-Wei Chang

Computer Science Seminar: Dr. Kai-Wei Chang from The University of Virginia

Host: Ian Davidson

When: Thursday, March 16th at 3:10pm

Where: 1131 Kemper Hall

Title: Structured Predictions: Practical Advancements and Applications

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, and they have been shown to be successful in many
application areas, such as natural language processing, computer
vision, and bioinformatics. There are two families of algorithms for
these problems: graphical model approaches and learning to search
approaches. In this talk, I will describe a collection of results that
improve several aspects of these approaches. Our results lead to
efficient learning algorithms for structured prediction models and for
online clustering models, which, in turn, support reduction in problem
size, improvements in training and evaluation speed, and improved
performance. We have used our algorithms to learn expressive models
from large amounts of annotated data and achieve state-of-the-art
performance on several natural language processing tasks.

Bio: Kai-Wei Chang is an assistant professor in the Department of
Computer Science at the University of Virginia. 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 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

1131 Kemper Hall

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