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Bin Yu | Department of Statistics
Current Website https://binyu.stat.berkeley.edu Office / Location 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
CiteSeerX — Abstract
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): spectral learning approach
Stat Berkeley Edu Binyu Ps Spectral Sbm Pdf Google Search
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Haiyan Huang, Professor
Amy E. Herr John D. & Catherine T. MacArthur Professor, University of California, Berkeley Verified email at berkeley.edu Jingyi Jessica Li Professor, Department of Statistics, University of California at Los Angeles (UCLA) Verified email at stat.ucla.edu
Stat 153 Fall 2010
Lectures: Cory 241. Tuesday/Thursday 12:30 - 2:00. Classroom and Computer Lab Section: Evans 344. Friday 9-11. Course Outline: An introduction to time series analysis in the time domain and frequency domain.
Statistics (STAT) < University of California, Berkeley
Prerequisites: Math 54 or 56 or 110 or Stat 89A or Physics 89 or both of EECS 16A and 16B with a C- or better, or Pass; Data/Stat/CompSci C100 with a C- or better, or Pass; and any of EECS 126, Data/Stat C140, Stat 134, IndEng 172, Math 106 with a C- or better, or Pass.
Bin Yu
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 Biology at UC …
Bin Yu
Bin Yu. Chancellor's Distinguished Professor and Class of 1936 Second Chair. Departments of Statistics and Electrical Engineering and Computer Sciences. UC Berkeley. Chan …
A Spectral Approach to Analysing Belief Propagation for 3 …
spectral properties of the adjacency matrix of the input graph. In addition, we point out that the analysis comprises a natural random graph model (namely, a 'planted solution' model). 1.2. Belief propagation and spectral techniques The main contribution of this paper is a rigorous analysis of BP for 3-colouring. We
Topics in Spectral Graph Methods
Course description:Spectral graph methods use eigenvalues and eigenvectors of matrices associated with a graph, e.g., adjacency matrices or Laplacian matrices, in order to understand the properties of the graph. They have a rich algorithmic and statistical theory, including connections with random
Statistics < University of California, Berkeley
The Statistics MA program prepares students for careers that require statistical skills. It focuses on tackling statistical challenges encountered by industry rather than preparing for a PhD. The program is for full-time students and is designed to be completed in two semesters (fall and spring). There is no way to transfer into the PhD program ...
Using Local Spectral Methods in Theory and in Practice
Local spectral methods: I Use random walks to nd locally-biased partitions in large graphs. I Can prove locally-biased versions of Cheeger's Inequality. I Scalable worst-case running time; non-trivial statistical properties. Success stories for local spectral methods: I Getting nearly-linear time Laplacian-based linear solvers.
Stat 153 Fall 2005
Solutions . 3 (c): ps, pdf . Due Friday, October 7 at noon, in Evans 330. Solutions . 4 (c): ps, pdf . Due Friday, November 4 at noon, in Evans 330. Data . 5 (c/2): ps, pdf . Due Tuesday, November 15 at 11:10, in Evans 70.
Multiway Spectral Clustering: A Margin-Based Perspective
Department of Statistics and Department of Electrical Engineering and Computer Science, University of California, Berkeley, USA (e-mail: [email protected]; [email protected]). The solutions of the relaxed problem are often re-ferred to as spectral embeddings and have applications outside of the clustering context (Belkin and ...
Spectral Learning
Spectral Learning - People @ EECS at UC Berkeley
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CiteSeerX — SPECTRAL CLUSTERING AND THE HIGH …
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Networks or graphs can easily represent a diverse set of data sources that are characterized by interacting units or actors. Social networks, representing people who communicate with each other, are one example. Communities or clusters of highly connected actors form an …
Impact of Regularization on Spectral Clustering
Impact of Regularization on Spectral Clustering Antony Joseph* and Bin Yu# * Department of Statistics, UC Berkeley and Berkeley Lawrence National Lab # Departments of …
Spectral clustering and the high-dimensional …
Spectral clustering and the high-dimensional Stochastic Block Model Bin Yu Departments of Statistics and EECS University of California at Berkeley [email protected] …
Current Faculty | Department of Statistics
Berkeley Statistics Annual Research Symposium (BSTARS) Seminars. Overview; Neyman Seminar; Probability Seminar; Statistics and Genomics Seminar; Tech Reports; Resources. ... Department of Statistics 367 Evans Hall, University of California Berkeley, CA 94720-3860 T 510-642-2781 | F 510-642-7892
Introduction to Time Series Analysis. Lecture 1.
Introduction to Time Series Analysis. Lecture 1. Peter Bartlett 1. Organizational issues. 2. Objectives of time series analysis. Examples. 3. Overview of the course.
Zhen Dong
Michael Mahoney Professor of Statistics, UC Berkeley Verified email at stat.berkeley.edu Amir Gholami Research Scientist, University of California, Berkeley Verified email at eecs.berkeley.edu Peng Huang Peking University Verified email at pku.edu
EECS 281B / STAT 241B Home Page
High-dimensional analysis of semidefinite programming relaxations for sparse principal component analysis, by A. A. Amini and M. J. Wainwright, To appear in the Annals of Statistics. Paper ; Eigenspectra of random kernel matrices, by Noureddine El Karoui, UC Berkeley Stat. technical report (2008)
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Statistics at UC Berkeley | Department of Statistics
Statistics at UC Berkeley | Department of Statistics
By Karl Rohe, Sourav Chatterjee and Bin Yu …
spectral clustering, the di erence is immaterial because both de nitions have the same eigenvectors. The spectral clustering algorithm addressed in this paper is de ned as …
Bin Yu | Department of Statistics
[email protected] Research Expertise and Interests statistical inference for high dimensional data and interdisciplinary research in neuroscience, remote sensing, and …
The Use of Statistics in Engineering and Food Industry
Statistics are an important part of our lives. The role of statistics in engineering and food industry is indispensable. Starting from designing a product, making a finished one and …
Spectral clustering and the high-dimensional …
insights and heuristics: spectral clustering is a convex relaxation of the Normalized Cut optimization problem (Shi and Malik, 2000), it can identify the connected components in …