Statistics at UC Berkeley | Department of Statistics

Statistics at UC Berkeley | Department of Statistics

Spectral clustering and the high-dimensional stochastic …

spectral clustering algorithm. For image segmentation, Shi and Malik (2000) sug-gested spectral clustering on an inferred network where the nodes are the pixels and the edges are determined by some measure of pixel similarity. In this way, spectral clustering has many similarities with the nonlinear dimension reduction

jasa_resub.dvi

Tao Shi is Assistant Professor, Department of Statistics, the Ohio State University, Columbus, OH 43210 (Email: [email protected]). Bin Yu is Professor, Department of Statistics, University of California, Berkeley, CA 94720 (Email: [email protected]). Eugene Clothiaux is Associate Professor, Department of Mete-

A unified framework for high-dimensional analysis of

Department of EECS1 Department of CS2 Department of Statistics3 UC Berkeley UT Austin UC Berkeley October 2010 Abstract High-dimensional statistical inference deals with models in which the the number of parame-ters p is comparable to or larger than the sample size n. Since it is usually impossible to obtain

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[email protected] ; [email protected] 1 Department of Industrial Engineering, Center for Statistical Science, Tsinghua University, Beijing, China 2 Department of Statistics, University of California, Berkeley, CA, USA 3 Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA 123 Author's ...

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‪Bin YU‬

‪Professor of Statistics and EECS, UC Berkeley‬ - ‪‪Cited by 43,515‬‬ - ‪Statistics‬ - ‪Machine Learning‬ - ‪Neuroscience‬ - ‪Genomics‬ - ‪Remote Sensing‬ ... UC Berkeley Verified email at stat.berkeley.edu. ... Spectral clustering and the high-dimensional stochastic blockmodel. K Rohe, S …

Bin Yu

I'm Bin Yu, the head of the Yu Group at Berkeley, which consists of 12-15 students and postdocs from Statistics and EECS. I was formally trained as a statistician, but my research interests …

Bin Yu | Department of Statistics

Berkeley Statistics Annual Research Symposium (BSTARS) Seminars. Overview; Neyman Seminar; Probability Seminar; Statistics and Genomics Seminar; Tech Reports; ... [email protected]. Dissertation. Some Results on Empirical Processes and Stochastic Complexity. Dissertation Advisor. Terence Speed, Lucien LeCam. Program.

ICML_final_08.dvi

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 identification of …

High-dimensionalcovariance estimation by minimizing

mentwise maximum-norm. This in turn allows us to derive convergence rates in Frobenius and spectral norms, with improvements upon existing results for graphs with maximum node degrees d = o(√ s). In our second result, we show that with probability converging to one, the estimate bΘ correctly specifies the zero pattern of the concentration

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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 …

Bin Yu | Department of Statistics

My current research focuses on practice, algorithm, and theory of statistical machine learning and causal inference. My group is engaged in interdisciplinary research with scientists from …

Stagewise Lasso

Peng Zhao [email protected] Department of Statistics University of Berkeley 367 Evans Hall Berkeley, CA 94720-3860, USA Bin Yu [email protected] Department of Statistics University of Berkeley 367 Evans Hall Berkeley, CA 94720-3860, USA Editor: Saharon Rosset Abstract

ICML_final_08.dvi

closely related to non-parametric spectral methods, such as spectral clustering (e.g., [8]) and Kernel Prin-cipal Components Analysis [11]. Those methods, as well as certain methods in manifold learning (e.g., [1]), construct a kernel matrix or a graph Laplacian ma-trix associated to a data set. The eigenvectors and

Retrieval Using MISR Data A Hierarchical Bayesian …

Yueqing Wang, Department of Statistics, University of California at Berke-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-versity of California at Berkeley, CA 94720-3860.

Regression and time series model selection in small …

Department of Statistics and Operations Research, New York University, New York NY 10003, U.S.A. AND CHIH-LING TSAI Division of Statistics, University of California, Davis, California 95616, U.S.A. SUMMARY A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models.

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Current research topics of my group cover sparse modeling (e.g. Lasso), structured sparsity (e.g. hierarchical and group and graph path), analysis and methods for spectral clustering for …

High-dimensionalcovarianceestimation byminimizing ℓ

Berkeley, CA 94720-1776 USA e-mail: [email protected] [email protected] [email protected] [email protected] Abstract: Given i.i.d. observations of a random vector X ∈ Rp, we study the problem of estimating both its covariance matrix Σ∗, and its inverse covariance or concentration matrix Θ ∗= (Σ )−1 ...

Bin Yu

Departments of Statistics and Electrical Engineering and Computer Sciences, ... 367 Evans Hall #3860 • Berkeley, CA 94720. phone: 510-642-2781 • fax: 510-642-7892 • [email protected]. Welcome. I'm Bin Yu, the head of the Yu Group at Berkeley, ... Google Sites. Report abuse ...

Spectral clustering and the high-dimensional Stochastic …

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 model with …

High-dimensionalcovariance estimation by minimizing

The area of high-dimensional statistics deals with estimation in the "large p, small n" setting, where p and n corre-spond, respectively, to the dimensionality of the data and the sample …

The Lasso under Heteroscedasticity

Jinzhu Jia [email protected] Karl Rohe [email protected] Department of Statistics University of California Berkeley, CA 94720, USA Bin Yu [email protected] Department of Statistics, and Department of Electrical Engineering and Computer Sciences University of California Berkeley, CA 94720, USA Editor: Abstract

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, U

Cloud Detection over Snow and Ice Using MISR Data

four spectral channels. Cloud detection is particularly di–cult in the snow- and ice-covered po- ... Berkeley, CA 94720-3860. Email: [email protected], [email protected] yDepartment of Meteorology, The Pennsylvania State University, University Park, PA 16802. Email: ... angle using four spectral bands (Figure 1). The view zenith ...

Detection of Daytime Arctic Clouds using MISR and …

content inherent in spectral and angular radiances, but these classifiers are impossible ∗Department of Statistics, The Ohio State University, Columbus, OH 43210-1247. Email: [email protected] †Department of Meteorology, Pennsylvania State University, University Park, PA 16802. Email: [email protected]

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stat berkeley edu binyu ps spectral sbm pdf

stat berkeley edu binyu ps spectral sbm pdf; ICML_final_08.dvi. closely related to non-parametric spectral methods, such as spectral clustering (e.g., [8]) and Kernel Prin-cipal Components Analysis [11]. Those methods, as well as certain methods in manifold learning (e.g., [1]), construct a kernel matrix or a graph Laplacian ma-trix associated ...

Minimax rates of estimation for high-dimensional …

[email protected] [email protected] Bin Yu1,2 [email protected] Departments of Statistics1, and EECS2 UC Berkeley, Berkeley, CA 94720 Statistics Technical Report October 1, 2009 Abstract Consider the standardlinear regressionmodelY = Xβ∗+w, where Y ∈ Rn is an observation