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.
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, 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: 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. 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 at least one computer hardware processor comprises a first computer hardware processor and a second computer hardware processor different from the first computer hardware processor, and wherein the processor-executable instructions cause: at least the first computer hardware processor to perform evaluation of the objective function at the first point; and at least the second computer hardware processor to perform evaluation of the objective function at the second point. 5. The system of claim 1, wherein the identifying comprises using an acquisition utility function obtained at least in part by calculating an expected value of an initial acquisition utility function with respect to potential values of the objective function at the first point. 6. The system of claim 1, wherein the likelihoods are obtained using a probabilistic model of the objective function, and wherein the processor-executable instructions further cause the at least one computer hardware processor to perform: updating the probabilistic model of the objective function using results of evaluating the objective function at the first point and/or the second point to obtain an updated probabilistic model of the objective function. 7. The system of claim 6, wherein the processor-executable instructions further cause the at least one computer hardware processor to perform: identifying, using the updated probabilistic model of the objective function, at least a third point at which to evaluate the objective function; and beginning evaluation of the objective function at least at the identified third point. 8. The system of claim 6, wherein the probabilistic model of the objective function comprises a Gaussian process or a neural network. 9. A method for use in connection with performing optimization using an objective function, the method comprising: 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. 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 at least one computer hardware processor comprises a first computer hardware processor and a second computer hardware processor different from the first computer hardware processor, and wherein the method comprises: using at least the first computer hardware processor to perform evaluation of the objective function at the first point; and using at least the second computer hardware processor to perform evaluation of the objective function at the second point. 13. The method of claim 9, wherein the identifying comprises using an acquisition utility function obtained at least in part by calculating an expected value of an initial acquisition utility function with respect to potential values of the objective function at the first point. 14. The method of claim 9, wherein the likelihoods are obtained using a probabilistic model of the objective function, and wherein the method further comprises: updating the probabilistic model of the objective function using results of evaluating the objective function at the first point and/or the second point to obtain an updated probabilistic model of the objective function. 15. At least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by 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, the method comprising: 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 the objective function at the second point. 16. The at least one non-transitory computer-readable storage medium of claim 15, 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. 17. The at least one non-transitory computer-readable storage medium of claim 15, 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. 18. The at least one non-transitory computer-readable storage medium of claim 15, wherein the at least one computer hardware processor comprises a first computer hardware processor and a second computer hardware processor different from the first computer hardware processor, and wherein the processor-executable instructions cause: at least the first computer hardware processor to perform evaluation of the objective function at the first point; and at least the second computer hardware processor to perform evaluation of the objective function at the second point. 19. The at least one non-transitory computer-readable storage medium of claim 15, wherein the identifying comprises using an acquisition utility function obtained at least in part by calculating an expected value of an initial acquisition utility function with respect to potential values of the objective function at the first point. 20. The at least one non-transitory computer-readable storage medium of claim 15, wherein the likelihoods are obtained using a probabilistic model of the objective function, and wherein the processor-executable instructions further cause the at least one computer hardware processor to perform: updating the probabilistic model of the objective function using results of evaluating the objective function at the first point and/or the second point to obtain an updated probabilistic model of the objective function.