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CS Student Colloquium: Boqing Gong - Kernel Methods for Domain Adaptation

Speaker: Boqing Gong, USC

Talk Title: Kernel Methods for Domain Adaptation

Series: Student Seminar Series

Abstract: The problem of domain adaptation occurs when the test data (of a target domain) and training data (of some source domain(s)) are generated by different distributions. It arises in a variety of applications, including computer vision, natural language process, speech recognition, etc.

In this talk, I will present some of our recent efforts on unsupervised domain adaptation using kernel methods. One cannot solve the domain adaptation problems given arbitrary source-target pairs. We have to explore the structures or properties in data, under which potentially successful solutions exist. Kernel methods are versatile in modeling such structures or properties. I will demonstrate several kernel methods ("kernel trick", discriminative multiple kernel learning, kernel embedding of distributions, etc.) which have been successfully used to model the structures of subspaces, landmarks, and latent domains. I will also present a sequential determinantal point process (seqDPP) with applications to supervised video summarization. This serves as the starting point of my future work on domain adaptation for video analysis.


Host: CS Department

More Information: GBQ.png

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

Audiences: Everyone Is Invited

Contact: Assistant to CS chair

This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.

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