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Sequential Preference-Based Optimization
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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.
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Cited by 1 Pith paper
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Local Preferential Bayesian Optimization
Local PBO methods using trust-region and derivative-informed local search on Laplace-approximated GP posteriors reduce cumulative regret versus global baselines in high-dimensional benchmarks.
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