Information Directed Sampling and Bandits with Heteroscedastic Noise. - Johannes Kirschner; Andreas Krause 001 (COLT, 2018)

Information-Directed Exploration for Deep Reinforcement Learning. - Nikolay Nikolov; Johannes Kirschner; Felix Berkenkamp; Andreas Krause 001 (ICLR (Poster), 2019)

Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces. - Johannes Kirschner; Mojmir Mutny; Nicole Hiller; Rasmus Ischebeck; Andreas Krause 001 (ICML, 2019)

Stochastic Bandits with Context Distributions. - Johannes Kirschner; Andreas Krause 001 (NeurIPS, 2019)

Experimental Design for Optimization of Orthogonal Projection Pursuit Models. - Mojmir Mutny; Johannes Kirschner; Andreas Krause 001 (AAAI, 2020)

Distributionally Robust Bayesian Optimization. - Johannes Kirschner; Ilija Bogunovic; Stefanie Jegelka; Andreas Krause 001 (AISTATS, 2020)

Information Directed Sampling for Linear Partial Monitoring. - Johannes Kirschner; Tor Lattimore; Andreas Krause 001 (COLT, 2020)

Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback. - Marc Jourdan; Mojmír Mutný; Johannes Kirschner; Andreas Krause 001 (ALT, 2021)

Asymptotically Optimal Information-Directed Sampling. - Johannes Kirschner; Tor Lattimore; Claire Vernade; Csaba Szepesvári (COLT, 2021)

Bias-Robust Bayesian Optimization via Dueling Bandits. - Johannes Kirschner; Andreas Krause 001 (ICML, 2021)

Bias-Robust Bayesian Optimization via Dueling Bandit. - Johannes Kirschner; Andreas Krause 001 (CoRR, 2021)

Information-Directed Sampling - Frequentist Analysis and Applications. - Johannes Kirschner (2021)

Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization. - Johannes Kirschner; Mojmír Mutný; Andreas Krause 001; Jaime Coello de Portugal; Nicole Hiller; Jochem Snuverink (CoRR, 2022)

Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning. - Volodymyr Tkachuk; Seyed Alireza Bakhtiari; Johannes Kirschner; Matej Jusup; Ilija Bogunovic; Csaba Szepesvári (AISTATS, 2023)

Near-optimal Policy Identification in Active Reinforcement Learning. - Xiang Li; Viraj Mehta; Johannes Kirschner; Ian Char; Willie Neiswanger; Jeff Schneider 001; Andreas Krause 001; Ilija Bogunovic (ICLR, 2023)

Regret Minimization via Saddle Point Optimization. - Johannes Kirschner; Seyed Alireza Bakhtiari; Kushagra Chandak; Volodymyr Tkachuk; Csaba Szepesvári (NeurIPS, 2023)

Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off. - Zichen Vincent Zhang; Johannes Kirschner; Junxi Zhang; Francesco Zanini; Alex Ayoub; Masood Dehghan; Dale Schuurmans (NeurIPS, 2023)

Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications. - Johannes Kirschner; Tor Lattimore; Andreas Krause 001 (J. Mach. Learn. Res., 2023)

Confidence Estimation via Sequential Likelihood Mixing. - Johannes Kirschner; Andreas Krause 001; Michele Meziu; Mojmir Mutny (CoRR, 2025)

Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomography. - Luis Barba; Johannes Kirschner; Tomas Aidukas; Manuel Guizar-Sicairos; Benjamín Béjar (CoRR, 2025)