Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks

Diksha Bhandari; Jakiw Pidstrigach; Sebastian Reich

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arXiv · 2309.04742

Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks

Diksha Bhandari·Jakiw Pidstrigach·Sebastian Reich

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Abstract

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 neural networks.

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BibTeX RIS

Diksha Bhandari, Jakiw Pidstrigach, Sebastian Reich. 2023-09-09. Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks. https://arxiv.org/abs/2309.04742

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