Models for High Dimensional Mixed Regression

The project team proposes to consider the mixed regression problem in high dimensions, under adversarial and stochastic noise. The team will consider convex optimization-based formulations with the aim of showing that it provably recovers the true solution. This agenda will seek to provide upper bounds on the recovery errors for both arbitrary noise and stochastic noise settings. The project team also will seek matching minimax lower bounds (up to log factors), showing that under certain assumptions, their algorithm is information-theoretically optimal. The team's preliminary results represent the first (and currently only known) tractable algorithm guaranteeing successful recovery with tight bounds on recovery errors and sample complexity. Mixture models treat observed data as a superposition of simple statistical processes. Thus they are particularly relevant in the transportation setting, when city-wide phenomena are often mixtures of simple processes (cut-through traffic, intra-city movement, etc.).


  • English


  • Status: Completed
  • Funding: $33000
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Project Managers:

    Bhat, Chandra

  • Performing Organizations:

    Data-Supported Transportation Operations and Planning Center

    University of Texas at Austin
    Austin, TX  United States  78701
  • Principal Investigators:

    Caramanis, Constantine

    Bhat, Chandra

  • Start Date: 20130930
  • Expected Completion Date: 20160930
  • Actual Completion Date: 20160930
  • Source Data: 109

Subject/Index Terms

Filing Info

  • Accession Number: 01579960
  • Record Type: Research project
  • Source Agency: Data-Supported Transportation Operations and Planning Center
  • Contract Numbers: DTRT13-G-UTC58
  • Files: UTC, RIP
  • Created Date: Oct 29 2015 2:34PM