Hyperparameter Optimization via Historical Data Pruning
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Solution Overview
Problem
Current algorithm configuration procedures struggle with efficiently optimizing hyperparameters for machine learning models, particularly in high-dimensional spaces with discontinuities and multiple objectives, leading to suboptimal performance and increased computational resource consumption.
Innovation Solution
A method that combines disparate training histories to prune unimportant parameters and low-quality values, using decision-tree-based models to infer importance and fill missing values, thereby reducing the complexity of the configuration space while maintaining high-quality configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If strong diversification mechanisms are used to explore the entire parameter configuration space, then the coverage of configuration space is improved, but the time required for optimization increases
Solution Approach 1:
The patent applies preliminary action by gathering and analyzing historical performance data before conducting new optimization searches. This pre-processing of configuration performance information allows the system to start from an informed position rather than exploring the entire configuration space from scratch, thereby reducing optimization time while maintaining thorough exploration coverage.
Solution Approach 2:
The patent implements feedback mechanisms by continuously collecting performance data from algorithm configurations and using this information to guide future search directions. The historical performance data serves as feedback that informs the optimization process, allowing the system to focus exploration on promising regions of the configuration space while avoiding previously identified poor configurations.
2Measurement precision
If the configuration space is thoroughly explored to find optimal parameters, then the accuracy of the algorithm is improved, but the computational resources consumed increase
Solution Approach 1:
The patent performs preliminary analysis of historical performance data to identify promising configuration regions before conducting exhaustive searches. This pre-screening based on past performance information allows the system to focus computational resources on configurations likely to yield high accuracy, rather than uniformly exploring the entire configuration space.
Solution Approach 2:
The patent changes the state of the optimization process by incorporating historical performance data as an additional input dimension. This transforms the optimization from a blind search to an informed search, where the system leverages past performance information to guide parameter selection and reduce the effective search space requiring exhaustive exploration.
3Measurement precision
If manual parameter tuning is performed to achieve satisfactory performance, then the algorithm performance is improved, but the time and effort required increase
Solution Approach 1:
The patent enables self-service by automating the parameter tuning process using historical performance data and optimization algorithms. Instead of requiring manual expert intervention to tune parameters, the system automatically analyzes historical data, identifies promising configurations, and performs optimization, thereby achieving high algorithm performance without manual tuning time investment.
Solution Approach 2:
The patent substitutes the mechanical process of manual parameter tuning with an automated computational system. The manual expert judgment and iterative adjustment process is replaced by algorithmic analysis of historical performance data and automated optimization routines, eliminating the time and effort previously required for manual tuning while maintaining or improving performance outcomes.
4Productivity
If heterogeneous historical performance data is utilized to accelerate optimization, then the speed of parameter tuning is improved, but the data processing complexity increases
Solution Approach 1:
The patent introduces an intermediary processing layer that standardizes and integrates heterogeneous historical performance data from multiple sources. This intermediary data processing framework handles the complexity of merging data with different formats, metrics, and quality levels, transforming raw heterogeneous data into a unified structure that can be efficiently used to accelerate optimization without exposing the complexity to the user.
Data Source
AI summary
In an embodiment, a computer combines first original hyperparameters and second original hyperparameters into combined hyperparameters. In each iteration of a binary search that selects hyperparameters, these are selected: a) important hyperparameters from the combined hyperparameters and b) based on an estimated complexity decrease by including only important hyperparameters as compared to the combined hyperparameters, which only one boundary of the binary search to adjust. For the important hyperparameters of a last iteration of the binary search that selects hyperparameters, a pruned value range of a particular hyperparameter is generated based on a first original value range of the particular hyperparameter for the first original hyperparameters and a second original value range of the same particular hyperparameter for the second original hyperparameters. To accelerate hyperparameter optimization (HPO), the particular hyperparameter is tuned only within the pruned value range to discover an optimal value for configuring and training a machine learning model.