publication . Preprint . 2017

Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning

Pallone, Stephen N.; Frazier, Peter I.; Henderson, Shane G.;
Open Access English
  • Published: 24 Feb 2017
We analyze the problem of learning a single user's preferences in an active learning setting, sequentially and adaptively querying the user over a finite time horizon. Learning is conducted via choice-based queries, where the user selects her preferred option among a small subset of offered alternatives. These queries have been shown to be a robust and efficient way to learn an individual's preferences. We take a parametric approach and model the user's preferences through a linear classifier, using a Bayesian prior to encode our current knowledge of this classifier. The rate at which we learn depends on the alternatives offered at every time epoch. Under certai...
free text keywords: Statistics - Machine Learning, Computer Science - Information Theory, Computer Science - Learning
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Funded by
NSF| BIGDATA: Mid-Scale: ESCE: Collaborative Research: Discovery and Social Analytics for Large-Scale Scientific Literature.
  • Funder: National Science Foundation (NSF)
  • Project Code: 1247696
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems
NSF| CAREER: Methodology for Optimization via Simulation: Bayesian Methods, Frequentist Guarantees, and Applications to Cardiovascular Medicine
  • Funder: National Science Foundation (NSF)
  • Project Code: 1254298
  • Funding stream: Directorate for Engineering | Division of Civil, Mechanical & Manufacturing Innovation
NSF| Stochastic Optimization Models and Methods for the Sharing Economy
  • Funder: National Science Foundation (NSF)
  • Project Code: 1537394
NSF| Cornell Center for Materials Research - CEMRI
  • Funder: National Science Foundation (NSF)
  • Project Code: 1120296
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Materials Research
NSF| Collaborative Research: Designing Functional Materials with Optimal Learning
  • Funder: National Science Foundation (NSF)
  • Project Code: 1536895
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