Online Matching, Black-box Optimization and Hyper-parameter Tuning

Machine learning algorithms form an integral part of modern data-driven platforms and systems. In the vehicular setting, examples range from platforms for matching -- allocating passengers to vehicles, matching cargo freight carriers – to onboard deep-learning based algorithms for driver-assist. While these algorithms adapt a range of parameters based on new information, what is common is that they typically need certain parameters to be fixed (the hyper-parameters) and are outside the learning framework. Due the high-dimensionality of the parameter space, hyper-parameter tuning (i.e. selecting these hyper-parameters) is a major hurdle in deploying algorithms. The project team proposes a principled approach for search and optimization of hyper-parameters.


  • English


  • Status: Active
  • Contract Numbers:


  • Sponsor Organizations:

    Department of Transportation

    Intelligent Transportation Systems Joint Program Office
    1200 New Jersey Avenue, SE
    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:

    Shakkottai, Sanjay

  • Start Date: 20180901
  • Expected Completion Date: 20200831
  • Actual Completion Date: 0
  • Source Data: 158

Subject/Index Terms

Filing Info

  • Accession Number: 01670185
  • Record Type: Research project
  • Source Agency: Data-Supported Transportation Operations and Planning Center
  • Contract Numbers: DTRT13-G-UTC58
  • Files: UTC, RIP
  • Created Date: May 23 2018 4:14PM