Techniques for use in connection with performing optimization using an objective function that maps elements in a first domain to values in a range. The techniques include using at least one computer hardware processor to perform: identifying a first point at which to evaluate the objective function at least in part by using an acquisition utility function and a probabilistic model of the objective function, wherein the probabilistic model depends on a non-linear one-to-one mapping of elements in the first domain to elements in a second domain; evaluating the objective function at the identified first point to obtain a corresponding first value of the objective function; and updating the probabilistic model of the objective function using the first value to obtain an updated probabilistic model of the objective function.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for use in connection with performing optimization using an objective function that maps elements in a first domain to values in a range, the system comprising: at least one computer hardware processor; and at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform: identifying a first point at which to evaluate the objective function at least in part by using an acquisition utility function and a probabilistic model of the objective function, wherein the probabilistic model depends on a non-linear one-to-one mapping of elements in the first domain to elements in a second domain; evaluating the objective function at the identified first point to obtain a corresponding first value of the objective function; and updating the probabilistic model of the objective function using the first value to obtain an updated probabilistic model of the objective function. 2. The system of claim 1, wherein the objective function relates values of hyper-parameters of a machine learning system to values providing a measure of performance of the machine learning system. 3. The system of claim 1, wherein the objective function relates values of a plurality of hyper-parameters of a neural network for identifying objects in images to respective values providing a measure of performance of the neural network in identifying the objects in the images. 4. The system of claim 1, wherein the processor-executable instructions further cause the at least one computer hardware processor to perform: identifying a second point at which to evaluate the objective function; evaluating the objective function at the identified second point to obtain a corresponding second value of the objective function; and updating the updated probabilistic model of the objective function using the second value to obtain a second updated probabilistic model of the objective function. 5. The system of claim 1, wherein the non-linear one-to-one mapping is bijective. 6. The system of claim 5, wherein the non-linear one-to-one mapping comprises a cumulative distribution function of a Beta distribution. 7. The system of claim 1, wherein the acquisition utility function is an integrated acquisition utility function. 8. The system of claim 1, wherein the probabilistic model of the objective function is obtained at least in part by using a Gaussian process or a neural network. 9. A method for use in connection with performing optimization using an objective function that maps elements in a first domain to values in a range, the method comprising: using at least one computer hardware processor to perform: identifying a first point at which to evaluate the objective function at least in part by using an acquisition utility function and a probabilistic model of the objective function, wherein the probabilistic model depends on a non-linear one-to-one mapping of elements in the first domain to elements in a second domain; evaluating the objective function at the identified first point to obtain a corresponding first value of the objective function; and updating the probabilistic model of the objective function using the first value to obtain an updated probabilistic model of the objective function. 10. The method of claim 9, wherein the objective function relates values of hyper-parameters of a machine learning system to values providing a measure of performance of the machine learning system. 11. The method of claim 9, wherein the objective function relates values of a plurality of hyper-parameters of a neural network for identifying objects in images to respective values providing a measure of performance of the neural network in identifying the objects in the images. 12. The method of claim 9, wherein the processor-executable instructions further cause the at least one computer hardware processor to perform: identifying a second point at which to evaluate the objective function; evaluating the objective function at the identified second point to obtain a corresponding second value of the objective function; and updating the updated probabilistic model of the objective function using the second value to obtain a second updated probabilistic model of the objective function. 13. The method of claim 9, wherein the non-linear one-to-one mapping is bijective. 14. The method of claim 13, wherein the non-linear one-to-one mapping comprises a cumulative distribution function of a Beta distribution. 15. The method of claim 14, wherein the acquisition utility function is an integrated acquisition utility function. 16. At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for use in connection with performing optimization using an objective function that maps elements in a first domain to values in a range, the method comprising: identifying a first point at which to evaluate the objective function at least in part by using an acquisition utility function and a probabilistic model of the objective function, wherein the probabilistic model depends on a non-linear one-to-one mapping of elements in the first domain to elements in a second domain; and evaluating the objective function at the identified first point to obtain a corresponding first value of the objective function. 17. The at least one non-transitory computer-readable storage medium of claim 16, wherein the objective function relates values of hyper-parameters of a machine learning system to values providing a measure of performance of the machine learning system. 18. The at least one non-transitory computer-readable storage medium of claim 16, wherein the processor-executable instructions further cause the at least one computer hardware processor to perform: identifying a second point at which to evaluate the objective function; evaluating the objective function at the identified second point to obtain a corresponding second value of the objective function; and updating the updated probabilistic model of the objective function using the second value to obtain a second updated probabilistic model of the objective function. 19. The at least one non-transitory computer-readable storage medium of claim 16, wherein the non-linear one-to-one mapping is bijective. 20. The at least one non-transitory computer-readable storage medium of claim 16, wherein the non-linear one-to-one mapping comprises a cumulative distribution function of a Beta distribution.