Filip Tronarp | DeepAI

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Featured Co-authors

Philipp Hennig
80 publications
Simo Särkkä
64 publications
Chris J. Oates
46 publications
Rui Gao
29 publications
Toni Karvonen
24 publications
Ángel F. García-Fernández
20 publications
Jon Cockayne
17 publications
Hans Kersting
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George Wynne
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Nathanael Bosch
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Ondřej Straka
4 publications

∙ 05/24/2023

Probabilistic Exponential Integrators

Probabilistic solvers provide a flexible and efficient framework for sim...

0 Nathanael Bosch, et al. ∙

∙ 06/17/2022

Orthonormal Expansions for Translation-Invariant Kernels

We present a general Fourier analytic technique for constructing orthono...

0 Filip Tronarp, et al. ∙

∙ 02/02/2022

Fenrir: Physics-Enhanced Regression for Initial Value Problems

We show how probabilistic numerics can be used to convert an initial val...

0 Filip Tronarp, et al. ∙

∙ 10/20/2021

Pick-and-Mix Information Operators for Probabilistic ODE Solvers

Probabilistic numerical solvers for ordinary differential equations comp...

0 Nathanael Bosch, et al. ∙

∙ 02/01/2021

A Probabilistic Taylor Expansion with Applications in Filtering and Differential Equations

We study a class of Gaussian processes for which the posterior mean, for...

0 Toni Karvonen, et al. ∙

∙ 12/15/2020

Calibrated Adaptive Probabilistic ODE Solvers

Probabilistic solvers for ordinary differential equations (ODEs) assign ...

12 Nathanael Bosch, et al. ∙

∙ 05/17/2020

Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes

In this letter, we propose a class of efficient, accurate and general me...

0 Rui Gao, et al. ∙

∙ 04/17/2020

Continuous-Discrete Filtering and Smoothing on Submanifolds of Euclidean Space

In this paper the issue of filtering and smoothing in continuous discret...

4 Filip Tronarp, et al. ∙

∙ 04/01/2020

Bayesian ODE Solvers: The Maximum A Posteriori Estimate

It has recently been established that the numerical solution of ordinary...

40 Filip Tronarp, et al. ∙

∙ 01/29/2020

Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions

Despite the ubiquity of the Gaussian process regression model, few theor...

20 Toni Karvonen, et al. ∙

∙ 03/20/2019

Iterated Extended Kalman Smoother-based Variable Splitting for L_1-Regularized State Estimation

In this paper, we propose a new framework for solving state estimation p...

0 Rui Gao, et al. ∙

∙ 10/08/2018

Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective

We formulate probabilistic numerical approximations to solutions of ordi...

10 Filip Tronarp, et al. ∙

∙ 09/13/2018

Gaussian process classification using posterior linearisation

This paper proposes a new algorithm for Gaussian process classification ...

6 Ángel F. García-Fernández, et al. ∙

∙ 05/29/2018

Iterative Statistical Linear Regression for Gaussian Smoothing in Continuous-Time Non-linear Stochastic Dynamic Systems

This paper considers approximate smoothing for discretely observed non-l...

0 Filip Tronarp, et al. ∙

∙ 03/15/2017

Student-t Process Quadratures for Filtering of Non-Linear Systems with Heavy-Tailed Noise

The aim of this article is to design a moment transformation for Student...

0 Jakub Prüher, et al. ∙