Bayesian Optimization and Semiparametric Models with Applications to Assistive Technology

By Jasper Snoek. 2014

55 citations recorded by the source corpus.

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Citations in the corpus

Scalable Bayesian Optimization Using Deep Neural Networks (2015, methodology): “Recent work (Gelbart et al., 2014; Snoek, 2013; Gramacy & Lee, 2010) has developed approaches for modeling unknown constraints in GP-based Bayesian optimization by learning a constraint classiﬁer and then discounting expected improvement by the probability of constraint violation.”
Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, methodology): “Snoek [142] and Gelbart et al. [56] proposed the weighted expected improvement criterion (wEI) that multiplies EI by the probability of satisfying the constraints: Where h ( x , D n ) is a Gaussian process with a Bernoulli observation model.”
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints (2015, background): “The next query x N +1 can then be deﬁned as that which maximizes the expected reduction in the differential entropy H [ · ] of the posterior on x (cid:63) .”
Bayesian Optimization with Unknown Constraints (2014, background): “Snoek (2013) discusses constrained Bayesian optimization for cases in which constraint violations arise from a failure mode of the objective function, such as a simulation crashing or failing to terminate.”
Constrained Bayesian Optimization for Automatic Chemical Design (2017, background): “vement with Constraints EIC may be thought of as EI that offers improvement only when a set of constraints are satisfied. EIC was first introduced by [100] and has received more recent attention from [111, 25]. It is defined as EIC(x)=EI(x)Pr(C(x)≥0) (2.29) where Pr(C(x) ≥0) denotes the probability ”