Parameter Tuning System Using Acquisition Function
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Solution Overview
Problem
Existing content platforms face challenges in efficiently tuning hundreds or thousands of parameters to optimize the provision of digital components with video content, due to limited resources and the impracticality of manual tuning or brute force methods.
Innovation Solution
A parameter tuning system that automatically identifies optimal parameter values by executing multiple iterations, generating models based on evaluation points, and using an acquisition function to prioritize exploration and exploitation, thereby selecting better parameter values more quickly and efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tuning or brute force methods are used to optimize parameter values, then comprehensive evaluation of parameters is possible, but resource consumption increases and tuning efficiency decreases
Solution Approach 1:
The system performs self-service by automatically selecting and evaluating parameter values without human intervention. The parameter tuning system autonomously executes experiments, analyzes results, and determines optimal parameter settings, eliminating the need for manual tuning while maintaining comprehensive evaluation capability.
Solution Approach 2:
The system performs preliminary action by pre-selecting a subset of promising parameter values based on initial analysis before conducting full experiments. This preliminary filtering reduces the search space and allows the system to focus resources on the most likely optimal parameters, improving efficiency while maintaining accuracy.
2Measurement precision
If brute force methods evaluate all possible parameter values, then optimal parameters can be found, but resource consumption becomes prohibitive
Solution Approach 1:
The system applies partial action by evaluating only a subset of parameter values rather than all possible combinations. It uses statistical sampling and intelligent search strategies to evaluate enough parameter configurations to find optimal settings with high confidence, without exhaustively testing every possibility, thus reducing resource consumption while maintaining optimization accuracy.
Solution Approach 2:
The system changes parameters strategically by adjusting evaluation criteria, confidence thresholds, and search depth based on available resources and problem characteristics. This dynamic parameter adjustment allows the system to adapt its evaluation thoroughness to balance accuracy requirements with resource constraints.
3Measurement precision
If more experiments are conducted to determine better parameter values, then optimization accuracy improves, but time and computational resources are consumed
Solution Approach 1:
The system uses feedback from each experiment to guide subsequent parameter selections and experimental design. By analyzing results from previous experiments, the system adjusts its search strategy, focuses on promising parameter regions, and determines when sufficient accuracy has been achieved, preventing unnecessary additional experiments and reducing tuning time.
Solution Approach 2:
The system dynamically adapts its experimental approach based on accumulated knowledge. It modifies evaluation criteria, adjusts confidence thresholds, and changes search intensity in real-time based on problem complexity and resource availability, allowing it to achieve optimal accuracy efficiently without rigid predetermined experiment counts.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automatically determining parameter values that control or affect provision of content by a content platform. In one aspect, evaluation points are identified for a parameter. 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. A first model is generated using the set of evaluation points. A second model is generated based on the first model and an acquisition function that is based on mean values and confidence intervals of the first model and a configurable exploration weight that controls a priority of exploration for evaluating the parameter. A next parameter value to evaluate is determined from the second model and the content platform is configured to use the next parameter value to provide digital components.