Predictive Model Uncertainty Decomposition for Active Learning
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Solution Overview
Problem
Predictive models in machine learning often fail to account for uncertainties, leading to incomplete understanding of how these uncertainties affect outputs, and typically focus on a single source of uncertainty, providing an incomplete picture of the modeling problem.
Innovation Solution
A predictive model is developed that accounts for both model-based and data-based uncertainties, allowing for alerts and self-adjustments to minimize output uncertainties, using Bayesian neural networks to decompose uncertainties into epistemic, aleatoric, and out-of-sample components, enhancing predictive accuracy and providing awareness of potential model bugs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predictive models account for multiple sources of uncertainty (model-based and data-based), then predictive accuracy and reliability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments uncertainty into distinct components: model-based uncertainty (epistemic uncertainty from the neural network) and data-based uncertainty (aleatoric uncertainty from the Bayesian hierarchical model). This segmentation allows each type of uncertainty to be modeled and analyzed separately, improving overall predictive reliability while managing complexity through structured decomposition of the uncertainty sources.
Solution Approach 2:
The patent employs a composite modeling approach by combining a neural network model with a Bayesian hierarchical model. This composite structure integrates multiple modeling paradigms to simultaneously capture different types of uncertainty, achieving higher reliability by leveraging the strengths of each individual model type while accounting for their combined complexity.
2Loss of information
If predictive models decompose uncertainties into multiple components (epistemic, aleatoric, out-of-sample), then understanding of modeling problems is improved, but measurement precision requirements increase
Solution Approach 1:
The patent applies segmentation by dividing total uncertainty into three distinct components: epistemic uncertainty (model-based), aleatoric uncertainty (data-based), and out-of-sample uncertainty. This segmentation enables comprehensive understanding of uncertainty sources while using specific measurement techniques appropriate for each component, thereby managing the precision requirements through targeted measurement approaches for each uncertainty type.
3Ease of operation
If predictive models provide holistic picture of uncertainties, then operational decision-making is improved, but loss of time in processing and analyzing uncertainties increases
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing uncertainty components during the model training phase. The Bayesian hierarchical model pre-characterizes data-based uncertainty, and the neural network pre-learns model-based uncertainty patterns. This preliminary computation enables rapid uncertainty assessment during operational decision-making, reducing the time required to process and analyze uncertainties when making real-time decisions.
Data Source
AI summary
Systems and methods are disclosed herein for improving machine learning of a data set. In one example, the method may include training a predictive model on an initial data set comprising labeled data, wherein the training is performed in an active learning system. The method may further include generating a set of parameters based on the training and introducing an unlabeled data set into the predictive model. According to some embodiments, the method may further include applying the set of parameters to the unlabeled data set, generating a set of predictions associated with the applied set of parameters and calculating a first uncertainty score and a second uncertainty score associated with the generated set of predictions. Moreover, the method may also include modifying the data set based on the first uncertainty score, and modifying the predictive model based on the second uncertainty score.