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Found 3 papers, 1 papers with code
Variational Inference on the Final-Layer Output of Neural Networks
In contrast, Bayesian neural networks provide good uncertainty quantification but optimizing them is time consuming due to the large parameter space.
On the Performance of Direct Loss Minimization for Bayesian Neural Networks
Direct Loss Minimization (DLM) has been proposed as a pseudo-Bayesian method motivated as regularized loss minimization.
Direct loss minimization algorithms for sparse Gaussian processes
The application of DLM in non-conjugate cases is more complex because the logarithm of expectation in the log-loss DLM objective is often intractable and simple sampling leads to biased estimates of gradients.
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