72 citations · 219 across the 19 of their papers we have counts for
19 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…
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…
Near-optimal Policy Identification in Active Reinforcement Learning
Xiang Li, Viraj Mehta, Johannes Kirschner +5
Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces. In cases where…
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…
Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization
Johannes Kirschner, Mojmir Mutný, Andreas Krause +3
Tuning machine parameters of particle accelerators is a repetitive and time-consuming task that is challenging to automate. While many off-the-shelf optimization algorithms are ava…
Bias-Robust Bayesian Optimization via Dueling Bandits
Johannes Kirschner, Andreas Krause
We consider Bayesian optimization in settings where observations can be adversarially biased, for example by an uncontrolled hidden confounder. Our first contribution is a reductio…
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback
Marc Jourdan, Mojmír Mutný, Johannes Kirschner +1
Combinatorial bandits with semi-bandit feedback generalize multi-armed bandits, where the agent chooses sets of arms and observes a noisy reward for each arm contained in the chose…
Asymptotically Optimal Information-Directed Sampling
Johannes Kirschner, Tor Lattimore, Claire Vernade +1
We introduce a simple and efficient algorithm for stochastic linear bandits with finitely many actions that is asymptotically optimal and (nearly) worst-case optimal in finite time…
Distributionally Robust Bayesian Optimization
Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka +1
Robustness to distributional shift is one of the key challenges of contemporary machine learning. Attaining such robustness is the goal of distributionally robust optimization, whi…
Information Directed Sampling for Linear Partial Monitoring
Johannes Kirschner, Tor Lattimore, Andreas Krause
Partial monitoring is a rich framework for sequential decision making under uncertainty that generalizes many well known bandit models, including linear, combinatorial and dueling…
Stochastic Bandits with Context Distributions
Johannes Kirschner, Andreas Krause
We introduce a stochastic contextual bandit model where at each time step the environment chooses a distribution over a context set and samples the context from this distribution.…
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…
Information Directed Sampling and Bandits with Heteroscedastic Noise
Johannes Kirschner, Andreas Krause
In the stochastic bandit problem, the goal is to maximize an unknown function via a sequence of noisy evaluations. Typically, the observation noise is assumed to be independent of…