Systems and methods for bayesian optimization using non-linear mapping of input

A system for optimizing performance of a machine learning system, the system comprising:

• at least one processor;
• and
• at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one processor, cause the at least one processor to perform:
• identifying a first set of hyper-parameter values at which to evaluate an objective function relating values of hyper-parameters of the machine learning system to performance of the machine learning system, the identifying performed at least in part by using a probabilistic model of the objective function, the probabilistic model of the objective function comprising a non-linear mapping of at least some of the values of the hyper-parameters from a first domain to a second domain;
• evaluating the objective function at the identified first set of hyper-parameter values, at least in part by executing the machine learning system when configured with the first set of hyper-parameter values;
• and
• updating the probabilistic model of the objective function based on the evaluation of the objective function at the first set of hyper-parameter values.

A method of optimizing performance of a machine learning system, the method comprising:

• identifying a first set of hyper-parameter values at which to evaluate an objective function relating values of hyper-parameters of the machine learning system to performance of the machine learning system, the identifying performed at least in part by using a probabilistic model of the objective function, the probabilistic model of the objective function comprising a non-linear mapping of at least some of the values of the hyper-parameters from a first domain to a second domain;
• evaluating the objective function at the identified first set of hyper-parameter values, at least in part by executing the machine learning system when configured with the first set of hyper-parameter values;
• and
• updating the probabilistic model of the objective function based on the evaluation of the objective function at the first set of hyper-parameter values.

At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method for optimizing performance of machine learning system, the method comprising:

• identifying a first set of hyper-parameter values at which to evaluate an objective function relating values of hyper-parameters of the machine learning system to performance of the machine learning system, the identifying performed at least in part by using a probabilistic model of the objective function, the probabilistic model of the objective function comprising a non-linear mapping of at least some of the values of the hyper-parameters from a first domain to a second domain;
• evaluating the objective function at the identified first set of hyper-parameter values, at least in part by executing the machine learning system when configured with the first set of hyper-parameter values;
• and
• updating the probabilistic model of the objective function based on the evaluation of the objective function at the first set of hyper-parameter values.