Tentative Schedule

Sunday, June 15, 2008

Morning: Conference Registration

8:30-11:30, Lobby, Siyuan Building

2:00-5:30, Lobby, Siyuan Building

7:00-9:00, Lobby, Jade Palace Hotel

Monday, June 16, 2008

All invited and contributed talks will be 35 and 20 minutes (including discussions) unless indicated otherwise. All talks will be in RoomS201, Graduate School, Zhong Guan Cun, CAS

Morning: Opening ceremony

8:30-8:50, Opening, Lei Guo, The Chinese Academy of Sciences, China

8:50-10:10

    Bayesian Methods and Constraint Modeling

    Chair: Xiaotong Shen, University of Minnesota, USA

  • Larry D. Brown (40 minutes), University of Pennsylvania, USA, In-Season Prediction of Batting Averages: A Field-test of Basic Empirical Bayes and Bayes Methodologies [Slides]
  • Bangang Hu (40 minutes), The Chinese Academy of Sciences, China, Issues about Embedding Prior Information onto Learning Machines--Example on Neural Network Models [Slides]

10:10-10:25, Break

10:25-12:10

    Gene Network and Sparse Learning

    Chair: Zhi Geng, Peking University, China

  • HongZhe Li, University of Pennsylvania, USA, Network-constrained Regularization and Variable Selection for Analysis of Genomic Data [Slides]
  • Wei Pan, University of Minnesota, USA, Networked Predictors in Penalized Regression with Application to Microarray Data [Slides]
  • Yongdai Kim, Seoul National University, South Korea, Incentive Sparse Estimator [Slides]

Afternoon

1:40-3:15

    Supervised Learning and Solution Paths

    Chair: Ji Zhu, University of Michigan, USA

  • Jerome H. Friedman (40 minutes), Stanford University,USA, Fast Sparse Regression and Classification [Slides]
  • Yoonkyung Lee, The Ohio State University, USA, Linear Programming for Feature Selection via Methods of Regularization [Slides]
  • Limin Yao (Contributed), Tsinghua University, China, The Entire Solution Path for Support Vector machine in Positive and Unlabeled Classification [Slides]

3:15-3:35, Break

3:35-5:20

    Data Mining

    Chair: HongZhe Li, University of Pennsylvania, USA

  • David Madigan (40 minutes), Columbia University, USA, Data Mining Issue in Drug Development [Slides]
  • An Chen (Contributed), The Chinese Academy of Sciences, China, A Web Mining Based Measurement and Monitoring Model of Urban Mass Panic in Emergency Management [Slides]
  • Xiao-Ling Lu (Contributed), Renming University of China, China, Mining E-Commerce Customers' Online Purchasing Behavior [Slides]
  • Lei Shi (Contributed), Yunnan University of Finance and Economics, China, Outlier Mining in Hierarchical Multilevel Data [Slides]

Evening: 6:00-8:00, Conference Banquet

Tuesday, June 17, 2008

Morning

8:30-9:45

    Estimation of Covariance Matrices

    Chair: Zhiliang Ying, Columbia University, USA

  • Iain Johnstone (40 minutes), Stanford University, USA, Approximate Null Distribution for the Largest Latent Root in Multivariate Analysis [Slides]
  • Yufeng Liu, The University of North Carolina at Chapel Hill, USA, Estimating Spatial Covariance using Penalized Likelihood with Weighted L1 Penalty [Slides]

9:45-10:00 Break

10:00-11:45

    Correlation and Manifold Learning

    Chair: Marina Meila, University of Washington, USA

  • Annie Qu, The Oregon State University, USA, Selecting Informative Correlation Structure in Multiple Sourced Correlation Data [Slides]
  • Ji Zhu, University of Michigan, USA, Partial Correlation Estimation by Joint Sparse Regression Models [Slides]
  • Ann B. Lee, Carnegie Mellon University, USA, Finding Low-dimensional Structure by Spectral Connectivity Analyses [Slides]

Afternoon

1:40-3:10

    Image Annotation and Principal Component Analysis

    Chair: Naisyin Wang, Texas A&M University, USA

  • Jieping Ye, Arizona State University, Computational Analysis of Drosophila Gene Expression Pattern Images [Slides]
  • Ping Ma, University of Illinois at Urbana-Champaign, USA, Statistical Journey to the Center of the Earth [Slides]
  • Lijun Liu (Contributed), Dalian Nationalities University, China, Adaptive Learning Algorithm for Principal Component Analysis with Data Dependent Learning Rate [Slides]

3:10-3:30, Break

3:30-5:15

    Ranking and Unsupervised Learning

    Chair: Baogang Hu, The Chinese Academy of Sciences, China

  • Marina Meila, University of Washington, USA, Consensus Finding, Exponential Models, and Infinite Rankings [Slides]
  • Hang Li, MSRA, China, Learning to Rank--Problem, Challenge and Opportunity [Slides]
  • Shuangge Ma, Yale University, USA, Variable Selection with Clustering Regularization [Slides]

Wednesday, June 18, 2008

Morning

8:30-10:25

    Classification

    Chair: Hang Li, MSRA, China

  • Jianqing Fan (40 minutes), Princeton University, USA, Covariance Learning [Slides]
  • Zhi-Ming Ma (40 minutes), The Chinese Academy of Sciences, China, Two-layer Statistical Learning [Slides]
  • Tian Zheng, Columbia University, USA, Feature Selection and Classification Based on k-Nearest-Neighbor Patterns [Slides]

10:25-10:45, Break

10:45-12:15

    Method of Penalization and Feature Selection

    Chair: Changshui Zhang, Tsinghua University, China

  • Cun-Hui Zhang, Rutgers University, USA, Information Theoretical Optimality of Variable Selection with Minimax Concave Penalty [Slides]
  • Xing Wang, Renmin University of China, China, Properties of Lasso Estimators in Generalized Linear Model [Slides]
  • Xingwei Tong (Contributed), Beijing Normal University, China, Variable Selection for Panel Count Data via Nonconcave Penalized Estimating Function [Slides]

Afternoon

1:40-3:25

    Mixture Learning and Data Recovery

    Chair: Annie Qu, The Oregon State University, USA

  • Mikhail Belkin, The Ohio State University, USA, Learning Probability Distributions with Eigenfunctions of Convolutions Operators [Slides]
  • Jinwen Ma, Peking University, China, Adaptive Model Selection on Finite Mixture [Slides]
  • Jianwei Ma, Tsinghua University, China, Data Recovery for Compressed Measurement [Slides]

3:25-3:45, Break

3:45-5:25

    Semisupervised Learning

    Chair: Jinwen Ma, Peking University, China

  • George Michailidis, University of Michigan, USA, Semisupervised Learning with Additive Models [Slides]
  • Changshui Zhang, Tsinghua University, China, Graph Based Semipservised Learning [Slides]
  • Shichao Zhang, Guangxi Normal University, China, Cost-sensitive Classification with Deficient Labeled Data [Slides]

Thursday, June 19, 2008

Morning

8:30-9:40

    Sparseness and Dimension Reduction in Regression

    Chair: Yuhong Yang, University of Minnesota, USA

  • Hansheng Wang, Peking University, China, Kernel Based Sliced Regression for Dimension reduction with Application in Earnings Pattern Mining [Slides]
  • Feng Liang, University of Illinois at Urbana-Champaign, USA, Local Sliced Inverse Regression [Slides]

9:40-10:00, Break

10:00-11:50

    Model Selection and Combination

    Chair: Jiashun Jin, Purdue University, USA

  • Tze Leung Lai (40 minutes), Stanford University, USA, A Consistent Model Selection Criterion for L2 Boosting in High-dimensional Sparse Linear Models [Slides]
  • Yuhong Yang, University of Minnesota, USA, Model Combination for Quantile Regression [Slides]
  • Harrison Zhou, Yale University, USA, Model Selection and Sharp Asymptotics [Slides]

Afternoon: Tour trip to the great wall for oversea participants