Research Description Bin Yu is a professor in the Department of Statistics. Her research interests are varied and included empirical processes, Markov Chain Monte Carlo methods, information theory and signal processing.
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Specifically, our algorithms outperform k-means by a large margin in terms of accuracy, and run several times faster than approximate spectral clustering based on the Nystrom method, with comparable accuracy and significantly smaller memory footprint.
UC Berkeley Chan-Zuckerberg Biohub Investigator Alumnus • Weill Neurohub Investigator mail: 367 Evans Hall #3860 • Berkeley, CA 94720 phone: 510-642-2021 • fax: 510-642-7892 • [email protected] Welcome I'm Bin Yu, the head of the Yu Group at Berkeley, which consists of 15-20 students and postdocs from Statistics and EECS.
Introduction. The problem of identifying communities (or clusters) inlarge networks is an important contemporary problem in statistics. Spectral clus-tering is one of the more popular techniques for such a purpose, chiefly due to itscomputational advantage and generality of application.
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Bin Yu - Bio Biography Bin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in the Departments of statistics and EECS, and Center for Computational …
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1 Introduction The problem of "neural coding" is to elucidate the representation and transformation of information in the nervous system. [17] An appealing way to attack neural coding is to take the otherwise vague notion of "information" to be defined in Shannon's sense, in terms of entropy. [20]
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409 Evans Hall Phone (510) 642-2021 Email [email protected] Research Expertise and Interests statistical inference for high dimensional data and interdisciplinary research in neuroscience, remote sensing, and text summarization Research Description https://vcresearch.berkeley.edu/faculty/bin-yu
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Announcements and Handouts ; Thurs, Feb 19: This webpage is no longer being updated: please go to the Stat 210b Piazza website. Wed, Feb 4: Chapter 4 on uniform laws is …
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Welcome I'm Bin Yu, the head of the Yu Group at Berkeley, which consists of 15-20 students and postdocs from Statistics and EECS. I was formally trained as a statistician, …
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Spectral clustering and the high-dimensional Stochastic Block Model Karl Rohe, Sourav Chatterjee and Bin Yu Department of Statistics University of California Berkeley, CA 94720, USA e-mail: [email protected] [email protected] [email protected] Abstract: Networks or graphs can easily represent a diverse …
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In this paper we propose a new method for estimat- ing parameters of a mixture distribution, which is closely related to non-parametric spectral methods, such as spectral clustering (e.g., [8]) and Kernel Prin- cipal Components Analysis [11].
Our findings not only extend and go beyond the intuitions underlyingexisting spectral techniques (e.g. spectral clustering and Kernel PrincipalComponents Analysis), but also provide insights about their usability andmodes of failure.
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