4 papers
Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans
Jingwen Wang, Johannes Kirschner, Paul Rolland +2
This paper presents a novel autonomous robotic assembly framework for constructing stable structures without relying on predefined architectural blueprints. Instead of following fi…
Principled Confidence Estimation for Deep Computed Tomography
Matteo Gätzner, Johannes Kirschner
We present a principled framework for confidence estimation in computed tomography (CT) reconstruction. Based on the sequential likelihood mixing framework (Kirschner et al., 2025)…
Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomography
Luis Barba, Johannes Kirschner, Tomas Aidukas +2
We introduce Diffusion Active Learning, a novel approach that combines generative diffusion modeling with data-driven sequential experimental design to adaptively acquire data for…
Confidence Estimation via Sequential Likelihood Mixing
Johannes Kirschner, Andreas Krause, Michele Meziu +1
We present a universal framework for constructing confidence sets based on sequential likelihood mixing. Building upon classical results from sequential analysis, we provide a unif…