Joint Label and Supervised Model Optimization
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Solution Overview
Problem
Current weak supervision methods for machine learning face challenges with synthetic labels being inaccurate and noisy, leading to sub-optimal performance due to independent optimization of label and supervised models, which can result in unsuitable synthetic labels for the supervised model.
Innovation Solution
The Integrated Synthetic Labeling Optimization (ISLO) approach jointly optimizes both the label model and the supervised model, considering their interaction to improve performance by creating multiple sets of synthetic labels using different subsets of heuristics and evaluating them against actual labels to select the best model for classification tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If synthetic labels are generated using independent optimization of label model and supervised model, then the labeling process becomes simpler and faster, but the accuracy and reliability of synthetic labels deteriorate
Solution Approach 1:
The patent merges the label model optimization and supervised model optimization into a single joint optimization framework. The objective function combines both labeling accuracy and classification performance, allowing the system to simultaneously optimize heuristic generation and model training. This integration ensures that synthetic labels are generated with consideration of their ultimate use in classification, resolving the contradiction between simple independent optimization and accurate joint optimization.
Solution Approach 2:
The patent implements feedback loops where the supervised model's performance on synthetic labels informs the label model's heuristic optimization, and vice versa. The joint optimization process continuously adjusts heuristics based on classification outcomes and refines model parameters based on label quality, creating a closed-loop system that improves both labeling accuracy and classification performance iteratively.
2Quantity of substance
If multiple heuristics are used to generate synthetic labels, then the coverage of labeled data increases, but the noise and inaccuracy in synthetic labels increases
Solution Approach 1:
The patent dynamically adjusts heuristic parameters and weights during joint optimization based on their performance contribution. Heuristics that generate noisy or inaccurate labels have their weights reduced or are pruned, while high-performing heuristics are emphasized. This parameter adjustment allows the system to maintain diverse heuristic coverage while filtering out noise through data-driven weight optimization.
Solution Approach 2:
The patent applies different quality standards and optimization criteria to different heuristics based on their individual performance characteristics. High-quality heuristics with proven accuracy are trusted more heavily, while lower-quality heuristics are applied more selectively or with reduced influence. This localized quality assessment allows the system to utilize diverse heuristics without uniformly accepting all their noise.
3Measurement precision
If manual labeling is performed to obtain ground truth data, then the accuracy of labeled data is improved, but the time and cost required increases significantly
Solution Approach 1:
The patent creates synthetic copies of manual labels through the joint optimization process. Instead of requiring extensive manual labeling, the system generates synthetic labels that are optimized to mimic the quality and distribution of manual ground truth. The joint optimization ensures these synthetic copies are accurate enough for effective model training, dramatically reducing the need for time-consuming manual labeling while maintaining label quality.
4Productivity
If limited labeled data is used for training, then the cost and time requirements are reduced, but the performance of the supervised learning model deteriorates
Solution Approach 1:
The patent performs preliminary joint optimization to generate high-quality synthetic training data before supervised model training. By pre-optimizing heuristics and generating accurate synthetic labels in advance, the system creates a robust training foundation that enables effective model learning from limited actual labeled examples. This preliminary preparation maximizes the utility of scarce labeled data.
Solution Approach 2:
The joint optimization process dynamically adjusts model parameters and heuristic weights to maximize performance on the limited labeled data available. The system learns optimal parameter configurations that leverage the small labeled set effectively, compensating for the limited data through intelligent parameter tuning and synthetic data augmentation generated through the optimized heuristics.
Data Source
AI summary
Techniques are disclosed relating to weakly supervised machine learning, which may be employed when there is a limited amount of labeled data available. A computer system may generate respective sets of synthetic labels for unlabeled data for a classification problem, where a given set of synthetic labels is produced by a corresponding one of a plurality of different label models. The computer system may then fit a set of supervised models, where each supervised model is fitted with one of the respective sets of synthetic labels to produce a respective set of predictions. The computer system may then evaluate the set of supervised models based on their respective set of predictions and using a set of labeled data for the classification problem. The evaluation may be used to select a particular supervised model and its corresponding label model.