Techniques for use in connection with performing optimization using a plurality of objective functions associated with a respective plurality of tasks. The techniques include using at least one computer hardware processor to perform: identifying, based at least in part on a joint probabilistic model of the plurality of objective functions, a first point at which to evaluate an objective function in the plurality of objective functions; selecting, based at least in part on the joint probabilistic model, a first objective function in the plurality of objective functions to evaluate at the identified first point; evaluating the first objective function at the identified first point; and updating the joint probabilistic model based on results of the evaluation to obtain an updated joint probabilistic model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for use in connection with performing optimization using a plurality of objective functions associated with a respective plurality of tasks, 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, based at least in part on a joint probabilistic model of the plurality of objective functions, a first point at which to evaluate an objective function in the plurality of objective functions; selecting, based at least in part on the joint probabilistic model, a first objective function in the plurality of objective functions to evaluate at the identified first point; evaluating the first objective function at the identified first point; and updating the joint probabilistic model based on results of the evaluation to obtain an updated joint probabilistic model. 2. The system of claim 1, wherein the first 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 first 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, based at least in part on the updated joint probabilistic model of the plurality of objective functions and, a second point at which to evaluate an objective function in the plurality of objective functions; selecting, based at least in part on the joint probabilistic model, a second objective function in the plurality of objective functions to evaluate at the identified first point; and evaluating the second objective function at the identified first point. 5. The system of claim 4, wherein the first objective function is different from the second objective function. 6. The system of claim 1, wherein the joint probabilistic model of the plurality of objective functions, models correlation among tasks in the plurality of tasks. 7. The system of claim 1, wherein the joint probabilistic model of the plurality of objective functions comprises a vector-valued Gaussian process. 8. The system of claim 1, wherein the joint probabilistic model comprises a covariance kernel obtained based, at least in part, on a first covariance kernel modeling correlation among tasks in the plurality of tasks and a second covariance kernel modeling correlation among points at which objective functions in the plurality of objective functions may be evaluated. 9. The system of claim 1, wherein the identifying is performed further based on a cost-weighted entropy-search utility function. 10. A method for use in connection with performing optimization using a plurality of objective functions associated with a respective plurality of tasks, the method comprising: using at least one computer hardware processor to perform: identifying, based at least in part on a joint probabilistic model of the plurality of objective functions, a first point at which to evaluate an objective function in the plurality of objective functions; selecting, based at least in part on the joint probabilistic model, a first objective function in the plurality of objective functions to evaluate at the identified first point; evaluating the first objective function at the identified first point; and updating the joint probabilistic model based on results of the evaluation to obtain an updated joint probabilistic model. 11. The method of claim 10, wherein the first objective function relates values of hyper-parameters of a machine learning system to values providing a measure of performance of the machine learning system. 12. The method of claim 10, further comprising: identifying, based at least in part on the updated joint probabilistic model of the plurality of objective functions, a second point at which to evaluate an objective function in the plurality of objective functions; selecting, based at least in part on the joint probabilistic model, a second objective function in the plurality of objective functions to evaluate at the identified first point; and evaluating the second objective function at the identified first point. 13. The method of claim 10, wherein the joint probabilistic model of the plurality of objective functions, models correlation among tasks in the plurality of tasks. 14. The method of claim 10, wherein the joint probabilistic model of the plurality of objective functions comprises a vector-valued Gaussian process. 15. The method of claim 10, wherein the joint probabilistic model comprises a covariance kernel obtained based, at least in part, on a first covariance kernel modeling correlation among tasks in the plurality of tasks and a second covariance kernel modeling correlation among points at which objective functions in the plurality of objective functions may be evaluated. 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 a plurality of objective functions associated with a respective plurality of tasks, the method comprising: identifying, based at least in part on a joint probabilistic model of the plurality of objective functions, a first point at which to evaluate an objective function in the plurality of objective functions; selecting, based at least in part on the joint probabilistic model, a first objective function in the plurality of objective functions to evaluate at the identified first point; evaluating the first objective function at the identified first point; and updating the joint probabilistic model based on results of the evaluation to obtain an updated joint probabilistic model. 17. The at least one non-transitory computer-readable storage medium of claim 16, wherein the first 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, based at least in part on the updated joint probabilistic model of the plurality of objective functions, a second point at which to evaluate an objective function in the plurality of objective functions; selecting, based at least in part on the joint probabilistic model, a second objective function in the plurality of objective functions to evaluate at the identified first point; and evaluating the second objective function at the identified first point; 19. The at least one non-transitory computer-readable storage medium of claim 16, wherein the joint probabilistic model of the plurality of objective functions comprises a vector-valued Gaussian process. 20. The at least one non-transitory computer-readable storage medium of claim 16, wherein the joint probabilistic model comprises a covariance kernel obtained based, at least in part, on a first covariance kernel modeling correlation among tasks in the plurality of tasks and a second covariance kernel modeling correlation among points at which objective functions in the plurality of objective functions may be evaluated.