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
Abstract
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...
Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Information Theory, Computer Science - Learning
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NSF| Collaborative Research: Designing Functional Materials with Optimal Learning
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  • Funder: National Science Foundation (NSF)
  • Project Code: 1536895
,
NSF| Cornell Center for Materials Research - CEMRI
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1120296
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Materials Research
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NSF| CAREER: Methodology for Optimization via Simulation: Bayesian Methods, Frequentist Guarantees, and Applications to Cardiovascular Medicine
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  • 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
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  • Funder: National Science Foundation (NSF)
  • Project Code: 1537394
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NSF| BIGDATA: Mid-Scale: ESCE: Collaborative Research: Discovery and Social Analytics for Large-Scale Scientific Literature.
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1247696
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems
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