72 citations · 219 across the 19 of their papers we have counts for
8 papers · 1 filter
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…
Regret Minimization via Saddle Point Optimization
Johannes Kirschner, Seyed Alireza Bakhtiari, Kushagra Chandak +2
A long line of works characterizes the sample complexity of regret minimization in sequential decision-making by min-max programs. In the corresponding saddle-point game, the min-p…
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning
Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner +3
A practical challenge in reinforcement learning are combinatorial action spaces that make planning computationally demanding. For example, in cooperative multi-agent reinforcement…
Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications
Johannes Kirschner, Tor Lattimore, Andreas Krause
Partial monitoring is an expressive framework for sequential decision-making with an abundance of applications, including graph-structured and dueling bandits, dynamic pricing and…
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off
Zichen Zhang, Johannes Kirschner, Junxi Zhang +4
A default assumption in reinforcement learning (RL) and optimal control is that observations arrive at discrete time points on a fixed clock cycle. Yet, many applications involve c…
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces
Johannes Kirschner, Mojmír Mutný, Nicole Hiller +2
Bayesian optimization is known to be difficult to scale to high dimensions, because the acquisition step requires solving a non-convex optimization problem in the same search space…
Information-Directed Exploration for Deep Reinforcement Learning
Nikolay Nikolov, Johannes Kirschner, Felix Berkenkamp +1
Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is…