When Has a Bayesian Neural Network Sampled Enough? Adaptive Inference Time with Statistical Guarantees

Bayesian neural network predictions are commonly approximated using a fixed number of Monte Carlo samples per input, without controlling the resulting error that comes from this finite sample. We propose the use of confidence sequences to dynamically determine how many samples are needed while maint