Multi-Objective AutoML Pipelines for Tradeoff-Aware Model Performance
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Solution Overview
Problem
Conventional autoML processes struggle to optimize multiple objectives simultaneously, failing to consider the inherent tradeoffs between them, resulting in unoptimized ML models that require significant domain-specific knowledge and are time-consuming and error-prone.
Innovation Solution
A controller that interacts with users to elicit tradeoffs between multiple objectives, generating ML pipelines that optimize these tradeoffs through an iterative process, using techniques like Pareto-dominance and depth-first search to produce undominated pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional autoML processes optimize multiple objectives simultaneously, then model performance across multiple metrics improves, but the complexity of managing tradeoffs between objectives increases
Solution Approach 1:
The patent introduces an intermediary system that automatically manages tradeoffs between multiple objectives. This intermediary component receives multiple objectives, determines appropriate tradeoffs between them, and generates optimized pipelines without requiring users to manually manage the complexity of balancing competing goals.
Solution Approach 2:
The system performs self-service by automatically determining tradeoffs between objectives without human intervention. The autoML process autonomously evaluates multiple objectives, identifies optimal tradeoffs, and generates pipelines that balance competing goals, eliminating the need for users to manually manage tradeoff complexity.
2Ease of operation
If automated pipeline generation is implemented, then the need for domain-specific knowledge is reduced, but the accuracy and optimization of generated pipelines deteriorates
Solution Approach 1:
The system changes parameters by automatically adjusting objective weights and tradeoff parameters during pipeline generation. This allows the system to optimize pipelines for multiple competing objectives simultaneously, achieving high-quality results that would otherwise require expert domain knowledge to manually tune.
Solution Approach 2:
The patent implements dynamics by allowing the system to adaptively adjust tradeoff parameters and objective priorities during the pipeline generation process. This dynamic adjustment enables the system to produce optimized pipelines that balance multiple objectives, achieving expert-level quality through automation rather than static, pre-configured parameters.
3Manufacturing precision
If multiple objectives are optimized with tradeoffs, then the quality of ML pipelines improves, but the time required for the autoML process increases
Solution Approach 1:
The system performs preliminary action by pre-determining tradeoff parameters and objective priorities before the main pipeline generation process. This upfront configuration allows the subsequent optimization to proceed more efficiently, reducing the overall time required while maintaining high pipeline quality across multiple objectives.
Solution Approach 2:
The patent implements continuity of useful action by maintaining parallel evaluation of multiple objectives throughout the pipeline generation process. Rather than sequentially optimizing each objective separately, the system continuously evaluates and balances all objectives simultaneously, reducing total processing time while achieving high-quality results.
Data Source
AI summary
A plurality of objectives is received for a given dataset for an automated machine learning (autoML) process. A set of tradeoffs for the plurality of objectives are received that distribute weights to respective objectives. Pipelines are provided for the dataset that optimize each of the plurality of objectives according to the set of tradeoffs.