Harvard College

University of Toronto

Socpra Sciences Et Genie S.e.c.

Université De Sherbrooke

Hugo Larochelle

Ryan P Adams

Roland Jasper Snoek

Richard Zemel

Kevin Swersky

Techniques for use in connection with performing optimization using an objective function. The techniques include using at least one computer hardware processor to perform: beginning evaluation of the objective function at a first point; before evaluating the objective function at the first point is completed: identifying, based on likelihoods of potential outcomes of evaluating the objective function at the first point, a second point different from the first point at which to evaluate the objective function; and beginning evaluation of the objective function at the second point.

Systems and methods for parallelizing Bayesian optimization

Techniques for use in connection with performing optimization using an objective function. The techniques include using at least one computer hardware processor to perform: identifying, using an integrated acquisition utility function and a probabilistic model of the objective function, at least a first point at which to evaluate the objective function; evaluating the objective function at least at the identified first point; and updating the probabilistic model of the objective function using results of the evaluating to obtain an updated probabilistic model of the objective function.

Systems and methods for Bayesian optimization using integrated acquisition functions

Growing community of inventors

Sherbrooke, Canada

Hugo Larochelle