Machine Learning Forecast Uncertainty via FTRL Gradient Updates
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Solution Overview
Problem
Existing machine learning techniques generate point estimates for inference scores without providing insight into the model's certainty about its predictions, leading to potential overreliance on examples with fewer past observations, which can result in larger confidence intervals and suboptimal decision-making.
Innovation Solution
A method to determine an uncertainty score by processing inputs with a machine-learned model, updating its parameters based on gradients, and calculating a difference in scores to estimate the effective number of similar examples seen, using the Follow-The-Regularized-Leader (FTRL) methodology to generate a measure of uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If point estimates are used for inference scores, then the model provides simple predictions, but the model cannot indicate its certainty about predictions
Solution Approach 1:
The patent introduces an intermediary uncertainty score that mediates between the simple point estimate and the true certainty. This uncertainty score acts as a separate indicator that does not complicate the primary prediction output while still conveying confidence information. The uncertainty score is computed through gradient-based perturbation analysis, adding a layer of information without requiring complex model architecture changes.
2Adaptability or versatility
If the model relies on examples with fewer past observations, then the model can make predictions on diverse examples, but the confidence interval becomes larger
Solution Approach 1:
The patent implements dynamic uncertainty estimation that adapts to each example based on its specific characteristics. The uncertainty score is computed dynamically through gradient perturbation, allowing the system to adjust confidence levels based on the number of similar past observations and the example's uniqueness. This enables the model to maintain versatility across diverse examples while providing reliable confidence indicators.
3Reliability
If uncertainty scores are computed for all examples, then the model can make informed decisions, but the computational complexity increases
Solution Approach 1:
The patent changes the parameter computation approach by using gradient-based perturbation analysis instead of complex Bayesian methods or ensemble techniques. The uncertainty score is derived from the change in loss function when parameters are perturbed, providing a computationally efficient method. This parameter change approach maintains reliability for informed decision-making while significantly reducing computational complexity compared to traditional uncertainty quantification methods.
Data Source
AI summary
Provided are systems and methods for generating a score for any model which can be updated online, regardless of model type architecture and parameters, leveraging relations between regret and uncertainty.