1 citations · 2 across the 2 of their papers we have counts for
5 papers
On the Theoretical Properties of Noise Correlation in Stochastic Optimization
Aurelien Lucchi, Frank Proske, Antonio Orvieto +2
Studying the properties of stochastic noise to optimize complex non-convex functions has been an active area of research in the field of machine learning. Prior work has shown that…
A Fourier State Space Model for Bayesian ODE Filters
Hans Kersting, Maren Mahsereci
Gaussian ODE filtering is a probabilistic numerical method to solve ordinary differential equations (ODEs). It computes a Bayesian posterior over the solution from evaluations of t…
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems
Hans Kersting, Nicholas Krämer, Martin Schiegg +3
Likelihood-free (a.k.a. simulation-based) inference problems are inverse problems with expensive, or intractable, forward models. ODE inverse problems are commonly treated as likel…
Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective
Filip Tronarp, Hans Kersting, Simo Särkkä +1
We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with non-linear measureme…
Convergence Rates of Gaussian ODE Filters
Hans Kersting, T. J. Sullivan, Philipp Hennig
A recently-introduced class of probabilistic (uncertainty-aware) solvers for ordinary differential equations (ODEs) applies Gaussian (Kalman) filtering to initial value problems. T…