1 citations · 3 across the 18 of their papers we have counts for
19 papers
Annealed Underdamped Langevin Dynamics
Laurenz Nagler, Alexander Falk, Andreas Habring
In sampling, different flavours of annealing, tempering, or other successive approximation approaches are widely used and studied. In this work we investigate annealed underdamped…
Lecture Notes: Convex Optimization
Andreas Habring
Lecture notes for a course on convex optimization taught by Andreas Habring at Graz University of Technology in 2026. The notes cover mathematical preliminaries, fundamental result…
Generating Physically Consistent Molecules with Energy-Based Models
Christoph Griesbacher, Lea Bogensperger, Andreas Habring +1
Molecules in equilibrium follow a Boltzmann distribution, making the underlying energy landscape a physically grounded modeling objective. However, such landscapes are difficult to…
Time-Inhomogeneous Preconditioned Langevin Dynamics
Alexander Falk, Laurenz Nagler, Andreas Habring +1
Langevin sampling from distributions of the form faces two major challenges: (global) mode coverage and (local) mode exploration. The first challenge is…
Forward-KL Convergence of Time-Inhomogeneous Langevin Diffusions
Andreas Habring, Martin Zach
Many practical samplers rely on time-dependent drifts -- often induced by annealing or tempering schedules -- to improve exploration and stability. This motivates a unified non-asy…
On the Time Derivative of the KL Divergence for a Generalized Langevin Annealing Scheme
Andreas Habring
Consider the Langevin diffusion process guided by the time-dependent probability density . Let be the…
Computation of a Consistent System Matrix for Cone-beam Computed Tomography
Josef Simbrunner, Clemens Krenn, Martin Zach +1
We propose a method for the computation of a consistent system matrix for two- and three-dimensional cone-beam computed tomography (CT). The method relies on the decomposition of t…
Accelerated Sampling with the Inertial Langevin Algorithm
Alexander Falk, Andreas Habring, Christoph Griesbacher +1
We consider the \emph{inertial Langevin algorithm} (ILA), a simple momentum-based method for sampling from Gibbs distributions of the form . ILA augments…
Energy-based models for inverse imaging problems
Andreas Habring, Martin Holler, Thomas Pock +1
In this chapter we provide a thorough overview of the use of energy-based models (EBMs) in the context of inverse imaging problems. EBMs are probability distributions modeled via G…
The Gaussian Latent Machine: Efficient Prior and Posterior Sampling for Inverse Problems
Muhamed Kuric, Martin Zach, Andreas Habring +2
We consider the problem of sampling from a product-of-experts-type model that encompasses many standard prior and posterior distributions commonly found in Bayesian imaging. We sho…
Diffusion at Absolute Zero: Langevin Sampling Using Successive Moreau Envelopes [conference paper]
Andreas Habring, Alexander Falk, Thomas Pock
In this article we propose a novel method for sampling from Gibbs distributions of the form with a potential . In particular, inspired by diffusion m…
Diffusion at Absolute Zero: Langevin Sampling using Successive Moreau Envelopes [journal paper]
Andreas Habring, Alexander Falk, Martin Zach +1
We propose a method for sampling from Gibbs distributions of the form by considering a family of approximations of the target density which is…
Ergodicity of Langevin Dynamics and its Discretizations for Non-smooth Potentials
Lorenz Fruehwirth, Andreas Habring
This article is concerned with sampling from Gibbs distributions using Markov chain Monte Carlo methods. In particular, we investigate Langevin dynamics in…
Neural-network-based regularization methods for inverse problems in imaging
Andreas Habring, Martin Holler
This review provides an introduction to - and overview of - the current state of the art in neural-network based regularization methods for inverse problems in imaging. It aims to…
Subgradient Langevin Methods for Sampling from Non-smooth Potentials
Andreas Habring, Martin Holler, Thomas Pock
This paper is concerned with sampling from probability distributions on admitting a density of the form , where with $K…
Posterior-Variance-Based Error Quantification for Inverse Problems in Imaging
Dominik Narnhofer, Andreas Habring, Martin Holler +1
In this work, a method for obtaining pixel-wise error bounds in Bayesian regularization of inverse imaging problems is introduced. The proposed method employs estimates of the post…
Unsupervised energy disaggregation via convolutional sparse coding
Christian Aarset, Andreas Habring, Martin Holler +1
In this work, a method for unsupervised energy disaggregation in private households equipped with smart meters is proposed. This method aims to classify power consumption as active…
A Note on the Regularity of Images Generated by Convolutional Neural Networks
Andreas Habring, Martin Holler
The regularity of images generated by convolutional neural networks, such as the U-net, generative networks, or the deep image prior, is analyzed. In a resolution-independent, infi…
A Generative Variational Model for Inverse Problems in Imaging
Andreas Habring, Martin Holler
This paper is concerned with the development, analysis and numerical realization of a novel variational model for the regularization of inverse problems in imaging. The proposed mo…