Department of EECS1 Department of CS2 Department of Statistics3 UC Berkeley UT Austin UC Berkeley October 2010 Abstract ... [44] and hyper-spectral imaging in ecology [38], also lead to high-dimensional data sets. 1. In the regime p " n, it is well known that consistent estimators cannot be obtained unless

Comparison of MISR aerosol optical thickness with AERONET measurements in Beijing metropolitan area Xin Jiang a, Yang Liu b, Bin Yu c, Ming Jiang a a LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, China b Harvard School of Public Health, Boston, MA 02215, U.S. c Department of Statistics, University of …

Submitted to the Annals of Statistics THE COMPOSITE ABSOLUTE PENALTIES FAMILY FOR GROUPED AND HIERARCHICAL VARIABLE SELECTION By Peng Zhao and Guilherme Rocha and Bin Yu† University of California at Berkeley Extracting useful information from high-dimensional data is an important focus of today's statistical …

She obtained her B.S. degree in Mathematics from Peking University in 1984, her M.A. and Ph.D. degrees in Statistics from the University of California at Berkeley in 1987 and 1990, respectively. She held faculty positions at the University of Wisconsin-Madison and Yale University and was a Member of Technical Staff at Bell Labs, Lucent.

The Annals of Statistics 2009, Vol. 37, No. 6B, 3960–3984 DOI: 10.1214/09-AOS700 c Institute of Mathematical Statistics, 2009 DATA SPECTROSCOPY: EIGENSPACES OF CONVOLUTION OPERATORS AND CLUSTERING By Tao Shi1, Mikhail Belkin2 and Bin Yu3 Ohio State University, Ohio State University and University of California, Berkeley

Subject: Image Created Date: 6/20/2008 11:36:43 AM

@WalmartLabs and University of California, Berkeley The performance of spectral clustering can be considerably improved via regularization, as demonstrated empirically in Amini et al. [ Ann. Statist. 41

INAUGURAL ARTICLE STATISTICS Veridical data science Bin Yua,b,c,d,1 and Karl Kumbiera aStatistics Department, University of California, Berkeley, CA 94720; bElectrical Engineering and Computer Sciences Department, University of California, Berkeley, CA 94720; cChan Zuckerberg Biohub, San Francisco, CA 94158; and dLawrence Berkeley …

Abstract. In this paper we develop a spectral frame-work for estimating mixture distributions, specifically Gaussian mixture models. In physics, spectroscopy is often …

The Ohio State University∗† and University of California, Berkeley ... Similarly to spectral clustering methods, Kernel Principal Component Analysis (Scho¨lkopf, et al. [12]) and spectral dimensionality reduction (e.g., Belkin and Niyogi [1]) seek lower dimensional representations of the data by

Amongst the 36 spectral radiances available on the Moderate Resolution Imag-ing Spectroradiometer (MODIS) seven of them are used operationally for detection of ... ∗Department of Statistics, The Ohio State University, Columbus, OH 43210-1247. Email: ... University of California, Berkeley, CA 94720-3860. Email: [email protected]

CA 94720 (Email: [email protected]). Eugene Clothiaux is Associate Professor, Department of Mete-orology, Pennsylvania State University, University Park, PA 16802 (Email: [email protected]). Amy Braver- ... in four spectral bands (blue, green, red, and near-infrared). The view zenith angles of the nine

Bin Yu. Chancellor's Distinguished Professor for Statistics, Electrical Engineering and Computer Science. Class of 1936 Second Chair. Email: [email protected]. Lab …

ley, CA 94720-3860 (E-mail: [email protected]). Xin Jiang, LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, China. Bin Yu, Department of Electrical Engineering and Computer Sciences, Uni- ... spectral images at 1.1 km resolution for the blue, green, and near-infrared bands, and at 275 m for the red band. Based on

The Annals of Statistics 2011, Vol. 39, No. 4, 1878–1915 DOI: 10.1214/11-AOS887 © Institute of Mathematical Statistics, 2011 SPECTRAL CLUSTERING AND THE HIGH ...

My past work covered research areas in empirical process theory, information theory (MDL), MCMC methods, signal processing, machine learning, high dimensional data inference …

from data, interpreted in a broad sense. Statistics as a discipline has its primary role as assisting this knowledge/information acquisition process in a principled and scienti c manner. IT data are massive or high-dimensional, no matter whether the forms are old (numeric) or new (text, images, videos, sound, and multi-media).

Departments of Statistics and EECS, University of California at Berkeley, Berkeley, CA 94720, USA. E-mail: [email protected] Reproducibility is imperative for any scientific discovery. More often than not, modern scientific findings rely on statistical analysis of high-dimensional data. At a minimum, reproducibility manifests itself in ...

This talk will give conditions for spectral clustering to correctly estimate the community membership of nearly all nodes. These asymptotic results are the rst clustering results …

that the approximation is better the sparser the precision matrix. In what follows, X is a Rn×p matrix containing in each of its n rows observations of the zero- mean random vector X with covariance matrixΣ. Denote by Xj the j-th entry of X and by XJ∗ the (p − 1) dimensional vector resulting from deleting Xj from X.For a given j, we can permute the …

The performance of spectral clustering can be considerably improved via regularization, as demonstrated empirically in Amini et al. [ Ann. Statist. 41 (2013) 2097–2122].

partment of Statistics∗ at University of California, Berkeley ... @kcl.ac.uk, [email protected]). Finan-cial support from EPSRC under grant EP/D053005/1 is gratefully acknowl-edged. Bin Yu thanks the National Science Foundation for grants NSF SES-0835531 (CDI) and NSFC (60628102). aim to reduce explicitly the effects of noise …

Spectral clustering is a popular and computationally feasible method to discover these communities. The Stochastic Block Model (Holland et al., 1983) is a social network …

The field of statistics indeed has been undergoing major changes over the last few decades. There has been consider-able discussion and introspection within the statistics commu-nity regarding the challenges and the future of the discipline (see, e.g., Lindsay, Kettenring, and Siegmund 2004). In this ar-

[email protected] [email protected] Garvesh Raskutti †Bin Yu,! [email protected] [email protected] Departmentof Statistics†, and Department of Electrical Engineeringand Computer Sciences! University of California, Berkeley Berkeley, CA 94720 November21, 2008 Abstract

Department of Computer Science and Engineering, Ohio State University Bin Yu [email protected] Department of Statistics, University of California Berkeley Abstract In this paper we develop a spectral frame-work for estimating mixture distributions, specifically Gaussian mixture models. In physics, spectroscopy is often used for the

Bin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in the Departments of statistics and EECS, and Center for Computational Biology, and serves as a senior advisor at the Simons Institute for the Theory of Computing, all at UC Berkeley. She obtained her BS Degree in

Cloud Detection over Snow and Ice Using MISR Data. Clouds play a major role in Earth's climate and cloud detection is a crucial step in the processing of satellite observations in …

In addition to her work to develop and propagate her work to develop the PCS framework to make data science more trustworthy and transparent, Bin Yu has also played an …

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