Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks

• URL: http://arxiv.org/abs/2202.09664v1
• Date: Sat, 19 Feb 2022 19:31:36 GMT
• Title: Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks
• Authors: Kinjal Patel, Steven Waslander
• Abstract summary: We propose a network architecture capable of reliably estimating uncertainty of regression based predictions without sacrificing accuracy. We achieve this by breaking down the learning of prediction and prediction interval (PI) estimations into a two-stage training process. We compare the proposed method with current state-of-the-art uncertainty quantification algorithms on synthetic datasets and UCI benchmarks, reducing the error in the predictions by 23 to 34%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a network architecture capable of reliably estimating uncertainty of regression based predictions without sacrificing accuracy. The current state-of-the-art uncertainty algorithms either fall short of achieving prediction accuracy comparable to the mean square error optimization or underestimate the variance of network predictions. We propose a decoupled network architecture that is capable of accomplishing both at the same time. We achieve this by breaking down the learning of prediction and prediction interval (PI) estimations into a two-stage training process. We use a custom loss function for learning a PI range around optimized mean estimation with a desired coverage of a proportion of the target labels within the PI range. We compare the proposed method with current state-of-the-art uncertainty quantification algorithms on synthetic datasets and UCI benchmarks, reducing the error in the predictions by 23 to 34% while maintaining 95% Prediction Interval Coverage Probability (PICP) for 7 out of 9 UCI benchmark datasets. We also examine the quality of our predictive uncertainty by evaluating on Active Learning and demonstrating 17 to 36% error reduction on UCI benchmarks.

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