Corpus ID: 6349913

Sequential Preference-Based Optimization

@article{Dewancker2018SequentialPO,
  title={Sequential Preference-Based Optimization},
  author={Ian Dewancker and Jakob Bauer and M. McCourt},
  journal={ArXiv},
  year={2018},
  volume={abs/1801.02788}
}
Many real-world engineering problems rely on human preferences to guide their design and optimization. We present PrefOpt, an open source package to simplify sequential optimization tasks that incorporate human preference feedback. Our approach extends an existing latent variable model for binary preferences to allow for observations of equivalent preference from users. 

References

SHOWING 1-10 OF 16 REFERENCES
Interactive Preference Learning of Utility Functions for Multi-Objective Optimization
Gaussian Process Preference Elicitation
Preference learning with Gaussian processes
Active Preference Learning with Discrete Choice Data
Preferential Bayesian Optimization
A Sample-Efficient Black-Box Optimizer to Train Policies for Human-in-the-Loop Systems With User Preferences
Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration
Ties in Paired-Comparison Experiments: A Generalization of the Bradley-Terry Model
Sequential Model-Based Optimization for General Algorithm Configuration
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