Machine Learning Parameter Optimization via Early Stopping
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Solution Overview
Problem
Complex systems with many adjustable parameters pose challenges in optimizing performance due to their black-box nature, where function evaluations are expensive and gradients or other information are not readily available, making it difficult to efficiently approach the global optimum.
Innovation Solution
A computer-implemented method using non-parametric regression and transfer learning to determine early-stopping of evaluations and dynamically switch between black-box optimization techniques, leveraging Gaussian Process regressors to suggest optimal parameter values for system optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive parameter evaluation is performed to ensure optimal performance, then performance optimization is improved, but computational resource expenditure increases
Solution Approach 1:
The patent performs preliminary actions by conducting prior evaluations of parameter variants before the main optimization process. These prior evaluations provide baseline performance data that guides subsequent optimization steps, allowing the system to focus computational resources on promising parameter configurations rather than exhaustively evaluating all possibilities.
Solution Approach 2:
The patent implements feedback mechanisms through intermediate evaluations during the optimization process. By continuously monitoring performance metrics and using this feedback to adjust the optimization trajectory, the system can identify underperforming parameter configurations early and redirect resources toward more promising directions, reducing overall computational expenditure.
Solution Approach 3:
The optimization system performs self-service by automatically determining when to terminate evaluations of parameter variants based on observed performance trends. Through early-stopping criteria, the system autonomously decides when sufficient performance information has been gathered, eliminating the need for exhaustive evaluation while maintaining optimization quality.
2Measurement precision
If evaluation duration is extended to capture complete performance data, then measurement precision is improved, but time consumption increases
Solution Approach 1:
The evaluation process performs self-service by automatically determining termination points based on observed performance trends. Through early-stopping criteria, the system autonomously decides when sufficient performance information has been gathered, eliminating the need for exhaustive evaluation while maintaining optimization quality.
Solution Approach 2:
The patent applies partial action by conducting evaluations for sufficient but not excessive duration. By using early-stopping criteria, the system performs just enough evaluation to capture meaningful performance trends without continuing unnecessarily long, achieving a balance between measurement precision and time efficiency.
3Adaptability or versatility
If optimization algorithm complexity is increased to handle black-box systems, then adaptability is improved, but computational overhead increases
Solution Approach 1:
The patent segments the optimization process into distinct phases: prior evaluation, intermediate evaluation, and final optimization. Each phase uses appropriately tailored algorithms and evaluation strategies, allowing the system to handle black-box complexity in manageable increments rather than requiring a single overly complex algorithm to handle all aspects simultaneously.
Solution Approach 2:
The optimization algorithm adapts to black-box systems by dynamically changing parameters such as evaluation budgets, termination criteria, and search strategies based on observed system behavior. This parameter adaptation allows the algorithm to maintain versatility across different black-box systems without requiring excessive structural complexity.
Data Source
AI summary
A computer-implemented method can include receiving, by one or more computing devices, one or more prior evaluations of performance of a machine learning model, the one or more prior evaluations being respectively associated with one or more prior variants of the machine-learning model, the one or more prior variants of the machine-learning model each having been configured using a different set of adjustable parameter values. The method can include utilizing, by the one or more computing devices, an optimization algorithm to generate a suggested variant of the machine-learning model based at least in part on the one or more prior evaluations of performance and the associated set of adjustable parameter values, the suggested variant of the machine-learning model being defined by a suggested set of adjustable parameter values.