Affine Invariant Ensemble Transform Methods to Improve Predictive
  Uncertainty in ReLU Networks
Diksha Bhandari Jakiw Pidstrigach Sebastian Reich
We consider the problem of performing Bayesian inference for logistic
regression using appropriate extensions of the ensemble Kalman filter. Two
interacting particle systems are proposed that sample from an approximate
posterior and prove quantitative convergence rates of these interacting
particle systems to their mean-field limit as the number of particles tends to
infinity. Furthermore, we apply these techniques and examine their
effectiveness as methods of Bayesian approximation for quantifying predictive
uncertainty in ReLU networks.