291 citations · 443 across the 4 of their papers we have counts for
4 papers
A General Framework for Constrained Bayesian Optimization using Information-based Search
José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams +2
We present an information-theoretic framework for solving global black-box optimization problems that also have black-box constraints. Of particular interest to us is to efficientl…
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
José Miguel Hernández-Lobato, Michael A. Gelbart, Matthew W. Hoffman +2
Unknown constraints arise in many types of expensive black-box optimization problems. Several methods have been proposed recently for performing Bayesian optimization with constrai…
Bayesian Optimization with Unknown Constraints
Michael A. Gelbart, Jasper Snoek, Ryan P. Adams
Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interes…
Learning Ordered Representations with Nested Dropout
Oren Rippel, Michael A. Gelbart, Ryan P. Adams
In this paper, we study ordered representations of data in which different dimensions have different degrees of importance. To learn these representations we introduce nested dropo…