Automatically determining parameter values

1. A computer-implemented method comprising:

executing a plurality of iterations to identify a parameter value for a parameter based on which a content platform controls provision of digital components with video content, wherein executing each iteration in the plurality of iterations includes:

identifying a set of evaluation points for the parameter, wherein each evaluation point includes an evaluated parameter value of the parameter and a metric value of a metric corresponding to the provision of digital components by the content platform, wherein the metric value of an evaluation point is determined from data generated by the content platform using the evaluated parameter value of the evaluation point to provide digital components;

generating a first model using the set of evaluation points;

generating mean values and confidence intervals of the first model;

generating a second model based on the first model and an acquisition function, wherein the acquisition function is based on the mean values of the first model, the confidence intervals of the first model, and a configurable exploration weight that controls a priority of exploration for evaluating the parameter;

determining, from the second model, a next parameter value to evaluate;

configuring the content platform to use the next parameter value to provide digital components with the video content; and

determining a next metric value based on data that results from the content platform using the next parameter value to provide digital components;

determining, from among the parameter values for the parameter and corresponding metric values determined during the plurality of iterations, a particular parameter value that either results in a highest metric value or satisfies a particular threshold; and

configuring the content platform using the particular parameter value to control or select, during production, digital components that are provided with the video content.

2. The computer-implemented method of claim 1 , further comprising determining, from the second model, at least one other parameter value to evaluate other than the next parameter value.

3. The computer-implemented method of claim 1 , wherein generating the first model comprises generating the first model as a model that fits the current set of evaluation points.

4. The computer-implemented method of claim 1 , wherein the exploration weight controls a priority of exploring new parameter values when determining, from the second model, the next parameter value to evaluate.

5. The computer-implemented method of claim 4 , wherein the exploration weight corresponds to a weight of confidence intervals in the acquisition function.

6. The computer-implemented method of claim 1 , wherein the exploration weight is higher in earlier parameter evaluation iterations and lower in later parameter evaluation iterations.

7. The computer-implemented method of claim 1 , wherein each evaluation point in an initial set of evaluation points includes a randomly-generated parameter value.

8. The computer-implemented method of claim 1 , wherein determining, from the second model, a next parameter value to evaluate includes determining a parameter value that has a corresponding highest acquisition function value.

9. A system, comprising:

one or more memory devices storing instructions; and

one or more data processing apparatus that are configured to interact with the one or more memory devices, and upon execution of the instructions, perform operations comprising:

executing a plurality of iterations to identify a parameter value for a parameter based on which a content platform controls provision of digital components with video content, wherein executing each iteration in the plurality of iterations includes:

identifying a set of evaluation points for the parameter, wherein each evaluation point includes an evaluated parameter value of the parameter and a metric value of a metric corresponding to the provision of digital components by the content platform, wherein the metric value of an evaluation point is determined from data generated by the content platform using the evaluated parameter value of the evaluation point to provide digital components;

generating a first model using the set of evaluation points;

generating mean values and confidence intervals of the first model;

generating a second model based on the first model and an acquisition function, wherein the acquisition function is based on the mean values of the first model, the confidence intervals of the first model, and a configurable exploration weight that controls a priority of exploration for evaluating the parameter;

determining, from the second model, a next parameter value to evaluate;

configuring the content platform to use the next parameter value to provide digital components with the video content; and

determining a next metric value based on data that results from the content platform using the next parameter value to provide digital components;

determining, from among the parameter values for the parameter and corresponding metric values determined during the plurality of iterations, a particular parameter value that either results in a highest metric value or satisfies a particular threshold; and

configuring the content platform using the particular parameter value to control or select, during production, digital components that are provided with the video content.

10. The system of claim 9 , wherein the one or more data processing apparatus that are configured to interact with the one or more memory devices, and upon execution of the instructions, perform operations further comprising:

determining, from the second model, at least one other parameter value to evaluate other than the next parameter value.

11. The system of claim 9 , wherein generating the first model comprises generating the first model as a model that fits the current set of evaluation points.

12. The system of claim 9 , wherein the exploration weight controls a priority of exploring new parameter values when determining, from the second model, the next parameter value to evaluate.

13. The system of claim 12 , wherein the exploration weight corresponds to a weight of confidence intervals in the acquisition function.

14. The computer-implemented method of claim 9 , wherein the exploration weight is higher in earlier parameter evaluation iterations and lower in later parameter evaluation iterations.

15. The system of claim 9 , wherein each evaluation point in an initial set of evaluation points includes a randomly-generated parameter value.

16. The system of claim 9 , wherein determining, from the second model, a next parameter value to evaluate includes determining a parameter value that has a corresponding highest acquisition function value.

17. A non-transitory computer readable medium storing instructions that, when executed by one or more data processing apparatus, cause the one or more data processing apparatus to perform operations, comprising:

executing a plurality of iterations to identify a parameter value for a parameter based on which a content platform controls provision of digital components with video content, wherein executing each iteration in the plurality of iterations includes:

identifying a set of evaluation points for the parameter, wherein each evaluation point includes an evaluated parameter value of the parameter and a metric value of a metric corresponding to the provision of digital components by the content platform, wherein the metric value of an evaluation point is determined from data generated by the content platform using the evaluated parameter value of the evaluation point to provide digital components;

generating a first model using the set of evaluation points;

generating mean values and confidence intervals of the first model;

generating a second model based on the first model and an acquisition function, wherein the acquisition function is based on the mean values of the first model, the confidence intervals of the first model, and a configurable exploration weight that controls a priority of exploration for evaluating the parameter;

determining, from the second model, a next parameter value to evaluate;

configuring the content platform to use the next parameter value to provide digital components with the video content; and

determining a next metric value based on data that results from the content platform using the next parameter value to provide digital components;

determining, from among the parameter values for the parameter and corresponding metric values determined during the plurality of iterations, a particular parameter value that either results in a highest metric value or satisfies a particular threshold; and

configuring the content platform using the particular parameter value to control or select, during production, digital components that are provided with the video content.

18. The non-transitory computer readable medium of claim 17 , wherein generating the first model comprises generating the first model as a model that fits the current set of evaluation points.

19. The non-transitory computer readable medium of claim 17 , wherein the exploration weight controls a priority of exploring new parameter values when determining, from the second model, the next parameter value to evaluate.

20. The non-transitory computer readable medium of claim 19 , wherein the exploration weight corresponds to a weight of confidence intervals in the acquisition function.