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.


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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