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Found 4 papers, 4 papers with code
A General Framework for Constrained Bayesian Optimization using Information-based Search
Of particular interest to us is to efficiently solve problems with decoupled constraints, in which subsets of the objective and constraint functions may be evaluated independently.
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
Unknown constraints arise in many types of expensive black-box optimization problems.
Bayesian Optimization with Unknown Constraints
Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions.
Learning Ordered Representations with Nested Dropout
To learn these representations we introduce nested dropout, a procedure for stochastically removing coherent nested sets of hidden units in a neural network.
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