Automated Machine Learning Algorithm Search System
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Solution Overview
Problem
Conventional machine learning research relies heavily on human-designed neural network architectures and sophisticated components, limiting the discovery of new algorithms and introducing human bias, while also requiring significant time and expertise.
Innovation Solution
A system that automatically searches for machine learning algorithms using basic mathematical operations as building blocks, allowing for the discovery of non-neural network algorithms and reducing human bias by defining a search space without assuming the existence of neural networks or gradients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional neural network architectures with hand-designed components are used, then the system can leverage existing expert knowledge and achieve reasonable performance, but the discovery of new algorithms is limited and human bias is introduced
Solution Approach 1:
The patent segments the machine learning algorithm into discrete, composable operations that can be independently selected and combined. Instead of treating neural network architectures as monolithic hand-designed structures, the system breaks them down into fundamental operations (e.g., convolution, pooling, activation functions) that can be systematically explored and recombined through automated search, enabling discovery of novel algorithmic compositions without requiring expert knowledge of each component.
Solution Approach 2:
The patent implements dynamic search spaces that can adapt during the algorithm discovery process. The system evolves the search space based on performance feedback, automatically adjusting which operations and configurations are explored. This dynamic approach allows the system to focus computational resources on promising regions of the search space while maintaining openness to novel algorithmic structures, thereby increasing adaptability without proportionally increasing overall complexity.
2Productivity
If sophisticated hand-designed components are used in neural networks, then the system can achieve good performance with existing knowledge, but significant time and expertise are required
Solution Approach 1:
The patent implements self-service automation where the system automatically performs algorithm selection, hyperparameter optimization, and architecture search without requiring manual intervention from experts. The automated search engine independently explores the algorithm space, evaluates candidate models, and selects optimal configurations based on performance metrics. This self-service capability dramatically reduces both the time required for algorithm development and the level of expert knowledge needed, as the system performs tasks that traditionally required human machine learning specialists.
3Adaptability or versatility
If the search space is constrained to neural network architectures, then the system can focus computational resources efficiently, but non-neural network algorithms cannot be discovered
Solution Approach 1:
The patent creates a universal search framework that can handle multiple algorithm types within a single system. The search space is designed to accommodate not only neural network architectures but also traditional machine learning algorithms, tree-based models, and other computational approaches. This universal framework uses a common evaluation and selection mechanism that works across different algorithm paradigms, allowing the system to discover diverse algorithm types without requiring separate specialized search processes for each algorithm category, thereby maintaining computational efficiency while expanding algorithmic diversity.
Data Source
AI summary
A method for searching for an output machine learning (ML) algorithm to perform an ML task is described. The method includes: receiving a set of training examples and a set of validation examples, and generating a sequence of candidate ML algorithms to perform the task. For each candidate ML algorithm in the sequence, the method includes: setting up one or more training parameters for the candidate ML algorithm by executing a respective candidate setup function, training the candidate ML algorithm by processing the set of training examples using a respective candidate predict function and a respective candidate learn function, and evaluating a performance of the trained candidate ML algorithm by executing the respective candidate predict function on the set of validation examples to determine a performance metric. The method includes selecting a trained candidate ML algorithm with the best performance metric as the output ML algorithm for the task.