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  • CS Colloquium: Kai-Wei Chang (UCLA) - Structured Predictions: Practical Advancements and Applications in Natural Language Processing

    Tue, Sep 26, 2017 @ 03:30 PM - 04:50 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Kai-Wei Chang, University of California, Los Angeles

    Talk Title: Structured Predictions: Practical Advancements and Applications in Natural Language Processing

    Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.

    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. 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, which, in turn, support reduction in problem size, improvements in
    training and evaluation speed. I will also discuss potential risks and challenges when using structured prediction models.

    Related information is at https://urldefense.proofpoint.com/v2/url?u=http-3A__www.cs.virginia.edu_-7Ekc2wc_talk_sp.html&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=LW6zU4yKxktEWcUPnmtKow&m=gw-3C-3UJqv9mPCsdDWaZHFxfXoQ6oXlSMsVWGL1xE0&s=l7eOcCL3YxMMSSFD4dVdUUMKTrGVB5Z8Dm0VD1cHVDM&e=

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

    Additional information is available at https://urldefense.proofpoint.com/v2/url?u=http-3A__kwchang.net&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=LW6zU4yKxktEWcUPnmtKow&m=gw-3C-3UJqv9mPCsdDWaZHFxfXoQ6oXlSMsVWGL1xE0&s=wik3X8kutwqg-z2gIVP9M7W-uRkf04mPpX4HhWqxCDM&e=.

    Host: Fei Sha

    Location: Henry Salvatori Computer Science Center (SAL) - 101

    Audiences: Everyone Is Invited

    Contact: Computer Science Department


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