1 code implementation • 30 Nov 2015 • José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani
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.
1 code implementation • 18 Feb 2015 • José Miguel Hernández-Lobato, Michael A. Gelbart, Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani
Unknown constraints arise in many types of expensive black-box optimization problems.
1 code implementation • 22 Mar 2014 • 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.
1 code implementation • 5 Feb 2014 • Oren Rippel, Michael A. Gelbart, Ryan P. Adams
To learn these representations we introduce nested dropout, a procedure for stochastically removing coherent nested sets of hidden units in a neural network.