Hyperparameter Projection Cache for Multi-Objective ML Tuning
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Solution Overview
Problem
Determining optimal hyperparameter values for machine learning models is challenging due to the complexity of hyperparameter optimization, as existing methods require extensive computational resources and time, and often fail to consider multiple objectives and constraints effectively.
Innovation Solution
A distributed machine learning system that uses multi-objective optimization to compute a Pareto front of optimized solutions, allowing for the automatic selection of hyperparameters based on multiple objectives such as accuracy, complexity, and constraints, such as misclassification rate or false positive rate, using a hyperparameter selection system that includes a user interface, selection manager, and worker system to evaluate and cache hyperparameter configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If numerous candidate models are trained to evaluate various hyperparameter values and combinations, then the quality of classification models can be improved, but the computing resources, computing time, and user time are significantly increased
Solution Approach 1:
The patent applies preliminary action by computing and caching projections of hyperparameter configurations before actual model training. The system pre-processes hyperparameter configurations to calculate their projections (transformed representations) and stores these in a cache. When evaluating new hyperparameter configurations, the system checks the cache first to avoid redundant computations, thereby reducing computing time while maintaining model quality evaluation.
Solution Approach 2:
The patent uses copying by creating transformed representations (projections) of hyperparameter configurations. Instead of training full models for every hyperparameter evaluation, the system creates simplified copies or projections of the hyperparameter space that can be evaluated more quickly. These projected configurations serve as surrogates that preserve essential information while reducing computational burden.
2Measurement precision
If numerous candidate models are trained to evaluate various hyperparameter values and combinations, then the quality of classification models can be improved, but the computing resources are significantly increased
Solution Approach 1:
The system performs preliminary computation of hyperparameter projections and caches these results before actual model training. By pre-computing and storing projected configurations, the system avoids redundant calculations during model evaluation, thereby reducing computing resources and energy consumption while still enabling thorough hyperparameter optimization.
Solution Approach 2:
The patent creates simplified copied representations (projections) of hyperparameter configurations that require fewer computational resources to evaluate. These projected copies allow the system to assess multiple hyperparameter combinations with reduced resource requirements compared to training full models for each configuration.
3Reliability
If the hyperparameter search space is extensively explored to find optimal values, then the model performance can be improved, but the user time and complexity of the process are increased
Solution Approach 1:
The patent introduces an intermediary mechanism - the projection cache - that mediates between the hyperparameter search space and model training. The cache stores pre-computed projections that serve as an intermediate layer, allowing the system to efficiently navigate the hyperparameter space without directly evaluating every possible configuration through full model training, thus reducing system complexity while maintaining performance.
Solution Approach 2:
The system creates simplified copied representations of hyperparameter configurations through projection. These copies allow for efficient exploration of the hyperparameter space by evaluating projected configurations rather than full model instances, reducing the apparent complexity of the tuning process while still enabling comprehensive search.
4Measurement precision
If multiple objectives and constraints are considered in hyperparameter optimization, then the quality of solutions can be improved, but the computational burden is increased
Solution Approach 1:
The system applies preliminary action by pre-computing and caching projections for multiple hyperparameter configurations. This allows the multi-objective optimization to evaluate numerous configurations across multiple objectives and constraints without performing the full computational work each time, thereby reducing the computational burden while maintaining solution quality.
Solution Approach 2:
The patent uses copied projected representations to evaluate multiple objectives and constraints. By working with these simplified copies rather than full model evaluations, the system can consider multiple competing objectives (such as accuracy, complexity, and constraints) with reduced computational burden.
Data Source
AI summary
Tuned hyperparameter values are determined for training a machine learning model. When a selected hyperparameter configuration does not satisfy a linear constraint, if a projection of the selected hyperparameter configuration is included in a first cache that stores previously computed projections is determined. When the projection is included in the first cache, the projection is extracted from the first cache using the selected hyperparameter configuration, and the selected hyperparameter configuration is replaced with the extracted projection in the plurality of hyperparameter configurations. When the projection is not included in the first cache, a projection computation for the selected hyperparameter configuration is assigned to a session. A computed projection is received from the session for the selected hyperparameter configuration. The computed projection and the selected hyperparameter configuration are stored to the first cache, and the selected hyperparameter configuration is replaced with the computed projection.