Web Javier Gonzalez
Numair Mansur · Personal Page
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NIPS Workshop on Bayesian Optimization December 9, 2017 Long Beach, USA
Bayesian optimization (BO) is a recent subfield of machine learning comprising a collection of methodologies for the efficient optimization of expensive black-box functions. BO techniques work by fitting a model to black-box function data and then using the model’s predictions to decide where to collect data next, so that the optimization problem can be solved using only a small number of function evaluations. The resulting methods are characterized by their high sample-efficiency when compared to alternati
As new BO methods have been developed, the area of applicability has been continuously expanding. While the problem of hyperparameter tuning permeates all disciplines, the field has moved towards more specific problems in science and engineering requiring of new advanced methodology. Today, Bayesian optimization is the most promising approach for accelerating and automating science and engineering. Therefore, we have chosen this year’s theme for the workshop to be “Bayesian optimization for science and engi
Web Javier Gonzalez
Numair Mansur · Personal Page
iPage