Faculty Candidate Talk: Kai-Wei Chang
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.
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 was one of the main contributors of a popular machine learning library, LIBLINEAR.