Learnable Uncertainty under Laplace Approximations
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Laplace approximations are classic, computationally lightweight means for constructing Bayesian neural networks (BNNs). As in other approximate BNNs, one cannot necessarily expect the induced predictive uncertainty to be calibrated. Here we develop a formalism to explicitly "train" the uncertainty in a decoupled way to the prediction itself. To this end we introduce uncertainty units for Laplace-approximated networks: Hidden units with zero weights that can be added to any pre-trained, point-estimated network. Since these units are inactive, they do not affect the predictions. But their presence changes the geometry (in particular the Hessian) of the loss landscape around the point estimate, thereby affecting the network's uncertainty estimates under a Laplace approximation. We show that such units can be trained via an uncertainty-aware objective, making the Laplace approximation competitive with more expensive alternative uncertainty-quantification frameworks.
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