ML Model Stability Assessment via Input Jittering
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Solution Overview
Problem
Conventional methods for assessing the stability of machine learning (ML) models focus on overall statistical distributions rather than individual input-output mappings, leading to inaccuracies.
Innovation Solution
The jittering approach introduces perturbations to input features to assess the stability of ML models on a point-wise basis, allowing for the tracking of changes that do not affect overall statistical distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods (cross-validation, Population Stability Index, Characteristic Stability Index) are used to assess model stability, then overall statistical distributions can be evaluated, but point-wise stability and local feature space regions cannot be accurately detected
Solution Approach 1:
The patent segments the stability assessment into point-wise evaluations of individual input samples rather than aggregate distribution analysis. By evaluating each sample's output variation independently through jittering, the method achieves fine-grained stability detection that conventional aggregate methods miss, directly resolving the contradiction between measurement precision and complexity.
Solution Approach 2:
The patent introduces jittered inputs as an intermediary mechanism to bridge the gap between input features and stability assessment. By perturbing inputs and measuring output variations, the method creates a measurable signal that reveals point-wise stability characteristics without requiring complex analysis of the model's internal behavior or overall distribution.
2Reliability
If conventional methods focus on aggregate statistical distributions, then overall model performance can be assessed, but changes in individual input-output mappings are overlooked
Solution Approach 1:
The patent applies segmentation by evaluating stability at the individual sample level rather than aggregating across the entire distribution. Each input sample is jittered independently and its output variation is measured separately, preserving point-wise stability information that would otherwise be lost in aggregate statistics.
Solution Approach 2:
The patent emphasizes local quality by assessing stability characteristics specific to each input sample and feature combination. The jittering approach allows different regions of the input feature space to be evaluated for their unique stability properties, revealing local variations in model behavior that aggregate methods cannot detect.
3Measurement precision
If jittering approach is applied to assess point-wise stability, then accuracy of stability assessment improves, but computational cost increases
Solution Approach 1:
The patent applies partial action by jittering only the relevant input features rather than all features, and by applying jittering to a representative sample rather than the entire dataset. This selective approach maintains measurement precision for stability assessment while reducing the computational burden compared to exhaustive evaluation.
Solution Approach 2:
The patent utilizes parameter changes by varying the jitter magnitude and perturbation distribution to optimize the balance between assessment accuracy and computational cost. By adjusting the degree and type of jittering, the method can achieve sufficient stability measurement precision with reduced computational resources.
Data Source
AI summary
Systems, apparatuses, and methods for training and/or assessing the stability of a machine learning (ML) model. Training and/or assessing the stability may include, for each input sample n of N input samples, for each perturbation q of Q perturbations: determining a perturbed input sample nq by perturbing the input sample n and using the ML model to obtain a perturbed output yqn based on the perturbed input sample nq. Training and/or assessing the stability may include, for each input sample n of the N input samples, aggregating the perturbed outputs yqn to obtain an aggregate perturbed output yn of the Q perturbations for the input sample n. Training the ML model may include updating one or more parameters of the ML model based on at least the aggregate perturbed outputs yn. Assessing the stability may include aggregating the aggregate perturbed outputs yn (or relative output variations ynrel).