7 citations · 8 across the 7 of their papers we have counts for
7 papers
Matrix-Free Least Squares Solvers: Values, Gradients, and What to Do With Them
Hrittik Roy, Søren Hauberg, Nicholas Krämer
This paper argues that the method of least squares has significant unfulfilled potential in modern machine learning, far beyond merely being a tool for fitting linear models. To re…
Numerically robust Gaussian state estimation with singular observation noise
Nicholas Krämer, Filip Tronarp
This article proposes numerically robust algorithms for Gaussian state estimation with singular observation noise. Our approach combines a series of basis changes with Bayes' rule,…
Adaptive Probabilistic ODE Solvers Without Adaptive Memory Requirements
Nicholas Krämer
Despite substantial progress in recent years, probabilistic solvers with adaptive step sizes can still not solve memory-demanding differential equations -- unless we care only abou…
Numerically Robust Fixed-Point Smoothing Without State Augmentation
Nicholas Krämer
Practical implementations of Gaussian smoothing algorithms have received a great deal of attention in the last 60 years. However, almost all work focuses on estimating complete tim…
A tutorial on automatic differentiation with complex numbers
Nicholas Krämer
Automatic differentiation is everywhere, but there exists only minimal documentation of how it works in complex arithmetic beyond stating "derivatives in " "d…
Gradients of Functions of Large Matrices
Nicholas Krämer, Pablo Moreno-Muñoz, Hrittik Roy +1
Tuning scientific and probabilistic machine learning models for example, partial differential equations, Gaussian processes, or Bayesian neural networks often relies on eva…