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Lukas Tatzel

2 accepted papers

2025

Debiasing Mini-Batch Quadratics for Applications in Deep Learning

ICLR 2025poster

Quadratic approximations form a fundamental building block of machine learning methods. E.g., second-order optimizers try to find the Newton step into the minimum of a local quadratic proxy to the objective function; and the second-order approximation of a network's loss function can be used to quan…

Cited by 1SourcePDFScholar
2024

Reparameterization invariance in approximate Bayesian inference

NeurIPS 2024spotlight

Current approximate posteriors in Bayesian neural networks (BNNs) exhibit a crucial limitation: they fail to maintain invariance under reparameterization, i.e. BNNs assign different posterior densities to different parametrizations of identical functions. This creates a fundamental flaw in the appli…

Cited by 4SourcePDFScholar