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PRODID:-//Apple Computer\, Inc//iCal 1.0//EN
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BEGIN:VEVENT
DESCRIPTION:Tuesday, November, 14th, 10 a.m. to 12 p.m., PHE 223\n
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Title: Improving machine learning algorithms with efficient data relevance discovery\n
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Abstract:\n
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This is the era of big data, where both challenges and opportunities lie ahead for the machine learning research. The data are created nowadays at an unprecedented pace with an unignorable cost in collecting, storing, and computing with the current scale of data. As the computational power that we possess gradually plateaus, it is an ever-increasing challenge to fully utilize the wealth of big data, where better data reduction techniques and scalable algorithms are the keys to a solution. We observe that to answer a certain query, the data are not equally important. Based on the models and the query, we provide efficient access to the numerical scores of the data points that represent their relevance in the current task. It enables us to wisely devote the computation resources to the important data, which improves the scalability and the reliability. We present our work under three applications: 1) tensor CP decomposition, 2) random-walk matrix-polynomial sparsification, where we provide an efficient access to the statistical leverage score for a faster numerical routine; and 3) matrix completability analysis, where we analyze the underlying completability structure for a more reliable estimation. \n
SEQUENCE:5
DTSTART:20171114T100000
LOCATION:PHE 223
DTSTAMP:20171114T100000
SUMMARY:PhD Defense- Dehua Cheng
UID:EC9439B1-FF65-11D6-9973-003065F99D04
DTEND:20171114T120000
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