Hyperparameter Objective Function Optimization
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Solution Overview
Problem
Standard hyperparameter tuning algorithms fail to adequately address instance level regressions and improvements, ignoring important goals like regression errors and considering only a single domain for training and evaluation, which can lead to models behaving differently after deployment and causing loss of perceived accuracy.
Innovation Solution
A computer-implemented method that initializes a machine learning algorithm with hyperparameter values and accesses a hyperparameter objective function defined across multiple domains, calculating domain scores based on instance-level improvements and regressions, excluding unstable instances, and incorporating an acceptable regression ratio to optimize hyperparameter tuning across varying domains and metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard hyperparameter tuning algorithms are used, then the tuning process is simple, but instance level regressions and improvements are not adequately addressed
Solution Approach 1:
The patent implements feedback mechanisms by tracking instance level predictions across multiple trials and domains. The system monitors which instances are correctly or incorrectly predicted, feeds this information back into the objective function calculation, and uses it to adjust hyperparameter selection in subsequent trials. This feedback loop ensures that instance level regressions are detected and addressed, improving reliability while maintaining operational simplicity through automated tracking.
Solution Approach 2:
The patent replaces traditional mechanical hyperparameter tuning approaches with an objective function-based system that automatically evaluates performance across multiple domains. Instead of manual trial-and-error or simple grid search, the system substitutes a computational objective function that incorporates domain scores, instance level metrics, and convergence criteria to automatically identify optimal hyperparameters, thereby addressing instance level issues without increasing operational complexity.
2Ease of manufacture
If a single domain is used for training and evaluation, then the model development is straightforward, but the model may behave differently after deployment across varying domains
Solution Approach 1:
The patent applies universality by designing an objective function that evaluates model performance across multiple domains simultaneously rather than optimizing for a single domain. The system trains and evaluates models on diverse datasets representing different domains, ensuring the hyperparameters selected are universally effective across varying conditions. This multi-domain approach maintains straightforward model development while significantly improving cross-domain adaptability and deployment consistency.
Solution Approach 2:
The patent segments the evaluation process into multiple independent domains, each with its own training and evaluation datasets. By dividing the search space into domain-specific subsets and evaluating performance separately for each domain, the system can identify hyperparameters that perform consistently across segments rather than optimizing for a single domain. This segmentation approach maintains development simplicity while ensuring versatile cross-domain performance.
3Measurement precision
If instance level metrics are tracked and stabilized, then prediction consistency improves, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential instance level metrics needed for evaluation—specifically, whether each instance is correctly or incorrectly predicted—rather than tracking all possible performance dimensions. By extracting and focusing on these critical instance level outcomes, the system achieves improved measurement precision for prediction consistency while avoiding the computational burden of tracking exhaustive performance metrics across all possible dimensions.
4Adaptability or versatility
If multiple domains are evaluated with domain weights, then cross-domain performance is optimized, but the objective function calculation becomes more complex
Solution Approach 1:
The patent manages objective function complexity by treating domain weights as adjustable parameters that can be modified based on domain importance. Rather than implementing a completely complex multi-domain evaluation framework, the system uses parameter changes to simplify calculation—allowing users to assign weights to domains based on their relative importance, thereby optimizing cross-domain performance while keeping the computational structure manageable through parameter-based control.
Data Source
AI summary
Techniques are disclosed herein for objective function optimization in target based hyperparameter tuning. In one aspect, a computer-implemented method is provided that includes initializing a machine learning algorithm with a set of hyperparameter values and obtaining a hyperparameter objective function that comprises a domain score for each domain that is calculated based on a number of instances within an evaluation dataset that are correctly or incorrectly predicted by the machine learning algorithm during a given trial. For each trial of a hyperparameter tuning process: training the machine learning algorithm to generate a machine learning model, running the machine learning model in different domains using the set of hyperparameter values, evaluating the machine learning model for each domain, and once the machine learning model has reached convergence, outputting at least one machine learning model.