Post Hoc Bias Compensation for ML Distribution Shifts
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Solution Overview
Problem
Machine learning models face challenges in adapting to varying conditions between training and test data sets, leading to performance mismatches due to unaccounted shifts in data distributions, which conventional methods fail to address effectively.
Innovation Solution
A bias compensation method that modifies the behavior of a first analytic engine service using a second service to optimize service bias by composing a new service based on the behavior model and true assessments, utilizing a compensator network to adjust output labels and improve performance on test datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained on a specific data distribution, then it achieves good performance on training data, but it fails to adapt to varying conditions in test data leading to performance mismatch
Solution Approach 1:
The patent applies preliminary action by pre-training a compensator network on the expected test data distribution before deployment. This compensator is trained in advance to learn the characteristics of the target domain, enabling the system to proactively adapt to distribution shifts rather than reacting to them during inference. The compensator network is prepared beforehand with knowledge of the test data characteristics, allowing it to compensate for mismatches between training and test distributions.
Solution Approach 2:
The patent introduces a compensator network as an intermediary component between the main classifier and the test data. This compensator acts as a mediator that transforms the main classifier's outputs to better align with the test data distribution. The compensator network serves as a bridge that adapts the training-time model to test-time conditions, resolving the mismatch without requiring retraining of the entire system.
2Adaptability or versatility
If conventional machine learning methods ignore differences between training and deployment environments, then the system remains simple, but it cannot compensate for expected shifts in data distribution
Solution Approach 1:
The patent segments the machine learning system into two distinct components: a main classifier and a compensator network. The main classifier handles the primary classification task while the compensator network specifically addresses distribution mismatches. This segmentation allows each component to have specialized functionality, with the compensator focusing solely on adapting to test data characteristics without interfering with the main classifier's core function.
Solution Approach 2:
The compensator network serves as an intermediary layer that sits between the main classifier and the final output. This intermediary component adds adaptability by transforming the main classifier's predictions to account for distribution shifts, while keeping the overall system architecture relatively simple and modular. The compensator can be trained separately and composed with the main classifier, avoiding the need to retrain the entire system.
Data Source
AI summary
A bias compensation method, system, and computer program product include modifying a behavior of a first analytic engine service with a second analytic engine service, where the first service accepts user submitted data and communicates an assessment of the data in a form of a label associated with the corresponding submitted data, where the second service accepts an input and communicates an assessment in a form of a label associated with the corresponding input, and where a behavior model of the first service and the second service includes a discrepancy between the output labels by each service with respect to true labels of data accepted, further including composing a new analytic engine service from the first service and the second service to optimize a service bias in terms of a test dataset based on the behavior model and the known true assessments.