Convex Hull Membership Testing for Data Shift Detection
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Solution Overview
Problem
Machine learning models face performance degradation due to data shifts between training and test data distributions, leading to poor performance in real-world applications, particularly due to target data shifts and covariate data shifts.
Innovation Solution
A method is developed to determine whether a target dataset belongs to the convex hull of multiple source datasets by computing a sample-based maximum mean discrepancy (MMD) measure and using a mirror descent optimization algorithm to assign sampling weights, thereby identifying the type of data shift and adapting the model accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained on training data, then the model learns patterns and relationships, but performance degrades when test data distribution shifts from training data
Solution Approach 1:
The patent applies preliminary action by pre-computing Maximum Mean Discrepancy (MMD) statistics and constructing confidence intervals during the training phase. These pre-computed statistical measures enable the system to predict and adapt to data distribution shifts before they occur in production, allowing the model to maintain reliability when encountering unseen test data distributions.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting model behavior based on computed MMD values. When the MMD between training and test data exceeds a threshold, the system modifies prediction weights or retraining strategies, effectively changing operational parameters to adapt to distribution shifts and maintain performance stability.
2Measurement precision
If the model is trained using labeled data with expected output, then the model learns supervised patterns, but cannot generalize well when target distribution changes
Solution Approach 1:
The patent implements feedback by continuously monitoring the MMD statistic between training and test data distributions. This feedback mechanism detects when target distribution shifts occur and triggers adaptive responses such as reweighting predictions or initiating retraining, enabling the supervised model to maintain generalization capability under distribution changes.
Solution Approach 2:
The patent applies dynamics by making the model's prediction behavior adaptive rather than static. The system dynamically adjusts prediction weights based on real-time MMD calculations, allowing the model to transition between different operational modes depending on whether data distribution shifts are detected, thereby improving versatility under target shift.
3Adaptability or versatility
If the model is trained using unlabeled data to find patterns, then the model discovers unsupervised structures, but performance is inconsistent when data distribution varies
Solution Approach 1:
The patent applies preliminary action by pre-computing MMD statistics and establishing confidence intervals during the unsupervised learning phase. These pre-established statistical benchmarks enable the system to maintain consistent prediction accuracy when data distribution varies, providing a foundation for reliable unsupervised pattern discovery across different domains.
4Adaptability or versatility
If reinforcement learning is used to train the model with reward system, then the model learns decision-making, but performance degrades when reward distribution shifts
Solution Approach 1:
The patent utilizes parameter changes by dynamically adjusting reinforcement learning policies based on MMD detection. When reward distribution shifts are detected through MMD analysis, the system modifies action selection probabilities or exploration parameters, enabling the decision-making model to adapt to new reward structures while maintaining reliability.
Data Source
AI summary
A method for determining whether a target dataset is in a convex hull of a plurality of source datasets is disclosed. The method includes obtaining the target dataset drawn from an unknown target distribution and the plurality of source datasets, wherein each source dataset is drawn from an unknown source distribution; assigning a sampling weight to each source distribution; constructing a mixed dataset comprising a plurality of samples drawn from source distributions according to the sampling weights of the source distributions; computing a sample based maximum mean discrepancy (MMD) measure between the target dataset and the mixed dataset; and determining that the target dataset is in the convex hull of the plurality of source datasets when the MMD measure is less than or equal to a threshold; otherwise determining that the target dataset is not in the convex hull of the plurality of source datasets.