Skip to main content
Cornell university
Cornell Statistics and Data Science Cornell Statistics and Data Science
  • About Us

    About Us
    Cornell's Department of Statistics and Data Science offers four programs at the undergraduate and graduate levels. Undergraduates can earn a BA in statistical science, social...

    Welcome to the Department of Statistics and Data Science
    History
    Facilities
    Statistics Graduate Society
    Recently Published Papers
  • Academics

    Academics

    Undergraduate
    PhD
    MPS
    PhD Minor in Data Science
    Courses & Course Enrollment
  • People

    People

    Faculty
    Field Faculty
    PhDs
    Emeritus Faculty
    Academic Staff
    Staff
    Research Areas of Expertise
    Statistical Consultants
  • News and Events

    News and Events

    Events
    News
  • Resources

    Resources

    Professional Societies and Meetings
    Affiliated Groups
    Career Services
    Cornell Statistical Consulting Unit
  • Alumni

    Alumni
    Cornell's Statistics and Data Science degrees prepare students for a wide variety of careers, from academia to industry.  See the After Graduation page for a general overview of...

    Alumni Profiles

Search form

You are here

  1. Home 
  2. Events 
  3. Statistics Seminars

Statistics Seminar Speaker: Yudong Chen, 10/21/2015

Event Layout

Wednesday Oct 21 2015

Statistics Seminar Speaker: Yudong Chen, 10/21/2015

4:15pm @ G01 Biotechnology
In Statistics Seminars

The Statistics Seminar Speaker for October 21 is Yudong Chen, an assistant professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University. His research interests include machine learning, high-dimensional and robust statistics, and convex optimization. Dr. Chen obtained a Ph.D. in Electrical and Computer Engineering in 2013 from The University of Texas at Austin. From 2013 to 2015 he was a postdoc in the EECS department at the University of California, Berkeley, and in 2014 was an academic visitor at the National University of Singapore. He received a B.S. and M.S. from Tsinghua University and worked as an intern at Raytheon BBN, IBM and Siemens. For more information, please visit his website.

Title: Fast Low-rank Estimation via Non-convex Projected Gradient Descent 

Abstract: Fitting a rank-r matrix to noisy data arise in many applications. The popular approach of nuclear/trace norm minimization, while in principle polynomial-time computable with strong (sometimes minimax optimal) statistical guarantees, is often computationally infeasible for large problems. In this talk, we consider a scalable approach via projected gradient descent over the (non-convex) space of n-by-r matrices. We develop a unified framework characterizing the convergence of such methods as well as the statistical properties of the resulting fixed point. Our results apply to a broad range of concrete models, including noisy matrix sensing, matrix completion with real and one-bit observations, matrix decomposition, structured PCA and graph clustering problems. For these problems non-convex projected gradient descent runs in near linear time, and provides statistical guarantees that match (and sometimes better than) the best known results provided by convex relaxation methods. 

Refreshments will be served after the seminar in 1181 Comstock Hall. 

 

Event Categories

  • Statistics Seminars
  • Special Events
  • Home
  • About Us
  • Contact Us
  • Careers
© Cornell University Department of Statistics and Data Science

1198 Comstock Hall, 129 Garden Ave., Ithaca, NY 14853

Social Menu

  • Facebook
  • Twitter
  • YouTube
Cornell Bowers CIS College of Computing and Information Science Cornell CALS ILR School

If you have a disability and are having trouble accessing information on this website or need materials in an alternate format, contact web-accessibility@cornell.edu for assistance.