| 2023 | AISTATS | 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 Szepesvri |
| 2023 | ICLR | Near-optimal Policy Identification in Active Reinforcement Learning. | Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic |
| 2021 | ALT | Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback. | Marc Jourdan, Mojmr Mutn, Johannes Kirschner, Andreas Krause |
| 2021 | COLT | Asymptotically Optimal Information-Directed Sampling. | Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvri |
| 2021 | ICML | Bias-Robust Bayesian Optimization via Dueling Bandits. | Johannes Kirschner, Andreas Krause |
| 2020 | AAAI | Experimental Design for Optimization of Orthogonal Projection Pursuit Models. | Mojmir Mutny, Johannes Kirschner, Andreas Krause |
| 2020 | AISTATS | Distributionally Robust Bayesian Optimization. | Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause |
| 2020 | COLT | Information Directed Sampling for Linear Partial Monitoring. | Johannes Kirschner, Tor Lattimore, Andreas Krause |
| 2019 | ICLR | Information-Directed Exploration for Deep Reinforcement Learning. | Nikolay Nikolov, Johannes Kirschner, Felix Berkenkamp, Andreas Krause |
| 2019 | ICML | Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces. | Johannes Kirschner, Mojmir Mutny, Nicole Hiller, Rasmus Ischebeck, Andreas Krause |
| 2018 | COLT | Information Directed Sampling and Bandits with Heteroscedastic Noise. | Johannes Kirschner, Andreas Krause |