Poster

Single Model Uncertainty Estimation via Stochastic Data Centering

Jayaraman Thiagarajan · Rushil Anirudh · Vivek Sivaraman Narayanaswamy · Timo Bremer

Hall J #243

Keywords: [ uncertainty quantification ] [ Active Learning ] [ calibration ] [ sequential optimization ] [ deep ensembles ]

[ Abstract ]
[ Poster [ OpenReview
Thu 1 Dec 9 a.m. PST — 11 a.m. PST

Abstract: We are interested in estimating the uncertainties of deep neural networks, which play an important role in many scientific and engineering problems. In this paper, we present a striking new finding that an ensemble of neural networks with the same weight initialization, trained on datasets that are shifted by a constant bias gives rise to slightly inconsistent trained models, where the differences in predictions are a strong indicator of epistemic uncertainties. Using the neural tangent kernel (NTK), we demonstrate that this phenomena occurs in part because the NTK is not shift-invariant. Since this is achieved via a trivial input transformation, we show that this behavior can therefore be approximated by training a single neural network -- using a technique that we call $\Delta-$UQ -- that estimates uncertainty around prediction by marginalizing out the effect of the biases during inference. We show that $\Delta-$UQ's uncertainty estimates are superior to many of the current methods on a variety of benchmarks-- outlier rejection, calibration under distribution shift, and sequential design optimization of black box functions. Code for $\Delta-$UQ can be accessed at github.com/LLNL/DeltaUQ

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