1 citations · 1 across the 5 of their papers we have counts for
5 papers
Position: Curvature Matrices Should Be Democratized via Linear Operators
Felix Dangel, Runa Eschenhagen, Weronika Ormaniec +3
Structured large matrices are prevalent in machine learning. A particularly important class is curvature matrices like the Hessian, which are central to understanding the loss land…
Debiasing Mini-Batch Quadratics for Applications in Deep Learning
Lukas Tatzel, Bálint Mucsányi, Osane Hackel +1
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 quadra…
Reparameterization invariance in approximate Bayesian inference
Hrittik Roy, Marco Miani, Carl Henrik Ek +4
Current approximate posteriors in Bayesian neural networks (BNNs) exhibit a crucial limitation: they fail to maintain invariance under reparameterization, i.e. BNNs assign differen…
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty
Lukas Tatzel, Jonathan Wenger, Frank Schneider +1
Non-conjugate Gaussian processes (NCGPs) define a flexible probabilistic framework to model categorical, ordinal and continuous data, and are widely used in practice. However, exac…
ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure
Felix Dangel, Lukas Tatzel, Philipp Hennig
Curvature in form of the Hessian or its generalized Gauss-Newton (GGN) approximation is valuable for algorithms that rely on a local model for the loss to train, compress, or expla…