AA5 – Variational Problems in Data-Driven Applications


AA5-4 (was EF1-22)

Bayesian Optimization and Inference for Deep Networks

Project Heads

Claudia Schillings, Vladimir Spokoiny

Project Members


Project Duration

01.01.2023 − 31.12.2024

Located at



Uncertainty quantification (UQ) and reliability of deep neuronal networks is an important research question. This project aims at developing of novel numerically efficient methods for inference and UQ analysis of DNN with theoretical guarantees. Particular issues to address are high parameter dimension and nonconvexity of the objective function. We propose a new insight on the problem using the recent progress in high dimensional Laplace approximation. A further goal is to apply the proposed methods to various application problems.

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