Events & News
CS Colloquium
| Category | Lecture |
| Date | Tuesday, February 17, 2015 |
| Time | 11:00 am |
| Concludes | 12:15 pm |
| Location | Gould-Simpson 906 |
| Details | Please join us for coffee and light refreshements at 11am in Gould-Simpson 906. Faculty Host: Mihai Surdeanu |
| Speaker | Kai-Wei Chang |
| Title | Ph.D Candidate in Computer Science |
| Affiliation | University of Illinois at Urbana-Champaign |
Practical Learning Algorithms for Structured Prediction Models
The desired output in many machine learning tasks is a structured
object such as a tree, a clustering of nodes, or a sequence. Learning
accurate prediction models for such problems requires training on
large amounts of data, making use of expressive features and
performing global inference that simultaneously assigns values to all
interrelated nodes in the structure. All these contribute to
significant scalability problems. We describe a collection of results
that address several aspects of these problems – by carefully
selecting and caching samples, structures, or latent items.
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.
Biography
Kai-Wei Chang is a doctoral candidate advised by Prof. Dan Roth in the
Department of Computer Science, University of Illinois at
Urbana-Champaign. His research interests lie in designing practical
machine learning techniques for large and complex data and applying
them to real world applications. He has been working on various topics
in Machine learning and Natural Language Processing, including
large-scale learning, structured learning, coreference resolution, and
relation extraction. 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) from the Department of Computer
Science at UIUC. He did internships at Microsoft CISL lab (2012) and
MSR (2013, 2014). He was one of the main contributors of a popular
machine learning library, LIBLINEAR.