skip to main content

Explore Research Products in the NSF-PAR

  • Home
  • Search Results
  • Page 1 of 6

Search for: All records

Creators/Authors contains: "Wang, Nan"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; ; ; ( , Coastal Engineering)
    Free, publicly-accessible full text available December 1, 2024
  2. ; ; ; ; ; ; ( , Applied Ocean Research)
    Free, publicly-accessible full text available December 1, 2024
  3. ; ; ( , Coastal Engineering)
    Free, publicly-accessible full text available August 1, 2024
  4. ; ; ( , Applied Ocean Research)
    Free, publicly-accessible full text available June 1, 2024
  5. ; ; ( , Coastal Engineering)
    Full Text Available 
  6. ; ; ( , Renewable and Sustainable Energy Reviews)
    Full Text Available 
  7. ; ; ; ( , Ocean Engineering)
    Full Text Available 
  8. ; ; ; ; ; ; ; ; ; ( , Chemical Engineering Journal)
    Full Text Available 
  9. ; ; ; ; ; ( , Journal of Network and Computer Applications)
    Full Text Available 
  10. ; ; ; ; ( , Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22)
    Most real-world optimization problems have multiple objectives. A system designer needs to find a policy that trades off these objectives to reach a desired operating point. This problem has been studied extensively in the setting of known objective functions. However, we consider a more practical but challenging setting of unknown objective functions. In industry, optimization under this setting is mostly approached with online A/B testing, which is often costly and inefficient. As an alternative, we propose Interactive Multi-Objective Off-policy Optimization (IMO^3). The key idea of IMO^3 is to interact with a system designer using policies evaluated in an off-policy fashion to uncover which policy maximizes her unknown utility function. We theoretically show that IMO^3 identifies a near-optimal policy with high probability, depending on the amount of designer's feedback and training data for off-policy estimation. We demonstrate its effectiveness empirically on several multi-objective optimization problems. 
    more » « less
    Full Text Available