Online Matching, Black-box Optimization and Hyper-parameter Tuning
| 项目名称： | Online Matching, Black-box Optimization and Hyper-parameter Tuning |
| 摘要： | 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. |
| 状态： | Active |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 项目负责人： | Bhat, Chandra R |
| 执行机构： | Data-Supported Transportation Operations and Planning Center |
| 主要研究人员： | Shakkottai, Sanjay |
| 开始时间： | 20180901 |
| 预计完成日期： | 20200831 |
| 实际结束时间： | 0 |
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