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SUMMARY:CS Yahoo! Machine Learning Seminar: Anshumali Shrivastava (Rice University) - Probabilistic Hashing for Scalable, Sustainable and Secure Machine Learning
DESCRIPTION:Speaker: Anshumali Shrivastava, Rice University
Talk Title: Probabilistic Hashing for Scalable, Sustainable and Secure Machine Learning
Series: Yahoo! Labs Machine Learning Seminar Series
Abstract: Large scale machine learning and data mining applications are constantly dealing with datasets at TB scale and the anticipation is that soon it will reach PB level. At this scale, simple data mining operations such as search, learning, and clustering become challenging.\n
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In this talk, we will start with a basic introduction to probabilistic hashing (or fingerprinting) and the classical LSH algorithm. Then I will present some of my recent adventures with probabilistic hashing in making large-scale machine learning practical. I will show how the\n
idea of probabilistic hashing can be used to significantly reduce the computations in classical machine learning algorithms such Deep Learning (using our recent success with asymmetric hashing for inner products). I will highlight the computational bottleneck, i.e. the hashing time, and will show an efficient variant of minwise hashing. In the end, if time permits, I will demonstrate the use of probabilistic hashing for obtaining practical privacy-preserving\n
algorithms.
Biography: Anshumali Shrivastava is an assistant professor in the computer science department at Rice University. His broad research interests include large scale machine learning, randomized algorithms for big data systems and graph mining. He is a recipient of 2017 NSF CAREER Award. His research on hashing inner products has won Best Paper Award at NIPS 2014 while his work on representing graphs got the Best Paper Award at IEEE/ACM ASONAM 2014. He obtained his PhD in computer science from Cornell University in 2015.
Host: Yan Liu
DTSTART:20170317T103000
LOCATION:RTH 526
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DTEND:20170317T113000
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