Search Results for author: Hans Kersting

Found 10 papers, 1 papers with code

An SDE for Modeling SAM: Theory and Insights

no code implementations19 Jan 2023 Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting, Aurelien Lucchi

We study the SAM (Sharpness-Aware Minimization) optimizer which has recently attracted a lot of interest due to its increased performance over more classical variants of stochastic gradient descent.

Explicit Regularization in Overparametrized Models via Noise Injection

1 code implementation9 Jun 2022 Antonio Orvieto, Anant Raj, Hans Kersting, Francis Bach

Injecting noise within gradient descent has several desirable features, such as smoothing and regularizing properties.

Anticorrelated Noise Injection for Improved Generalization

no code implementations6 Feb 2022 Antonio Orvieto, Hans Kersting, Frank Proske, Francis Bach, Aurelien Lucchi

Injecting artificial noise into gradient descent (GD) is commonly employed to improve the performance of machine learning models.

BIG-bench Machine Learning

A Fourier State Space Model for Bayesian ODE Filters

no code implementations17 Jul 2020 Hans Kersting, Maren Mahsereci

Gaussian ODE filtering is a probabilistic numerical method to solve ordinary differential equations (ODEs).

Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems

no code implementations ICML 2020 Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig

To address this shortcoming, we employ Gaussian ODE filtering (a probabilistic numerical method for ODEs) to construct a local Gaussian approximation to the likelihood.

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

no code implementations8 Oct 2018 Filip Tronarp, Hans Kersting, Simo Särkkä, Philipp Hennig

We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with non-linear measurement functions.

Convergence Rates of Gaussian ODE Filters

no code implementations25 Jul 2018 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.

Bayesian Filtering for ODEs with Bounded Derivatives

no code implementations25 Sep 2017 Emilia Magnani, Hans Kersting, Michael Schober, Philipp Hennig

Recently there has been increasing interest in probabilistic solvers for ordinary differential equations (ODEs) that return full probability measures, instead of point estimates, over the solution and can incorporate uncertainty over the ODE at hand, e. g. if the vector field or the initial value is only approximately known or evaluable.

Active Uncertainty Calibration in Bayesian ODE Solvers

no code implementations11 May 2016 Hans Kersting, Philipp Hennig

There is resurging interest, in statistics and machine learning, in solvers for ordinary differential equations (ODEs) that return probability measures instead of point estimates.

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