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ORCIDJohannes Kirschner
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2020 – today
- 2024
[i14]Johannes Kirschner, Seyed Alireza Bakhtiari, Kushagra Chandak, Volodymyr Tkachuk, Csaba Szepesvári:
Regret Minimization via Saddle Point Optimization. CoRR abs/2403.10379 (2024)- 2023
- [j1]Johannes Kirschner, Tor Lattimore, Andreas Krause:
Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications. J. Mach. Learn. Res. 24: 346:1-346:45 (2023) - [c14]Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner, Matej Jusup, Ilija Bogunovic, Csaba Szepesvári:
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning. AISTATS 2023: 6342-6370 - [c13]Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic:
Near-optimal Policy Identification in Active Reinforcement Learning. ICLR 2023 - [c12]Johannes Kirschner, Seyed Alireza Bakhtiari, Kushagra Chandak, Volodymyr Tkachuk, Csaba Szepesvári:
Regret Minimization via Saddle Point Optimization. NeurIPS 2023 - [c11]Zichen Vincent Zhang, Johannes Kirschner, Junxi Zhang, Francesco Zanini, Alex Ayoub, Masood Dehghan, Dale Schuurmans:
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off. NeurIPS 2023 - [i13]Johannes Kirschner, Tor Lattimore, Andreas Krause:
Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applications. CoRR abs/2302.03683 (2023) - [i12]Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner, Matej Jusup, Ilija Bogunovic, Csaba Szepesvári:
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2302.04376 (2023) - 2022
- [i11]Johannes Kirschner, Mojmír Mutný
, Andreas Krause, Jaime Coello de Portugal, Nicole Hiller, Jochem Snuverink:
Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization. CoRR abs/2203.13968 (2022) - [i10]Zichen Zhang, Johannes Kirschner, Junxi Zhang, Francesco Zanini, Alex Ayoub, Masood Dehghan, Dale Schuurmans:
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off. CoRR abs/2212.08949 (2022) - [i9]Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic:
Near-optimal Policy Identification in Active Reinforcement Learning. CoRR abs/2212.09510 (2022) - 2021
- [b1]Johannes Kirschner:
Information-Directed Sampling - Frequentist Analysis and Applications. ETH Zurich, Zürich, Switzerland, 2021 - [c10]Marc Jourdan, Mojmír Mutný, Johannes Kirschner, Andreas Krause:
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback. ALT 2021: 805-849 - [c9]Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári:
Asymptotically Optimal Information-Directed Sampling. COLT 2021: 2777-2821 - [c8]Johannes Kirschner, Andreas Krause:
Bias-Robust Bayesian Optimization via Dueling Bandits. ICML 2021: 5595-5605 - [i8]Marc Jourdan, Mojmír Mutný, Johannes Kirschner, Andreas Krause:
Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback. CoRR abs/2101.08534 (2021) - [i7]Johannes Kirschner, Andreas Krause:
Bias-Robust Bayesian Optimization via Dueling Bandit. CoRR abs/2105.11802 (2021) - 2020
- [c7]Mojmir Mutny, Johannes Kirschner, Andreas Krause:
Experimental Design for Optimization of Orthogonal Projection Pursuit Models. AAAI 2020: 10235-10242 - [c6]Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause:
Distributionally Robust Bayesian Optimization. AISTATS 2020: 2174-2184 - [c5]Johannes Kirschner, Tor Lattimore, Andreas Krause:
Information Directed Sampling for Linear Partial Monitoring. COLT 2020: 2328-2369 - [i6]Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause:
Distributionally Robust Bayesian Optimization. CoRR abs/2002.09038 (2020) - [i5]Johannes Kirschner, Tor Lattimore, Andreas Krause:
Information Directed Sampling for Linear Partial Monitoring. CoRR abs/2002.11182 (2020) - [i4]Johannes Kirschner, Tor Lattimore, Claire Vernade, Csaba Szepesvári:
Asymptotically Optimal Information-Directed Sampling. CoRR abs/2011.05944 (2020)
2010 – 2019
- 2019
- [c4]Nikolay Nikolov, Johannes Kirschner, Felix Berkenkamp, Andreas Krause:
Information-Directed Exploration for Deep Reinforcement Learning. ICLR (Poster) 2019 - [c3]Johannes Kirschner, Mojmir Mutny, Nicole Hiller, Rasmus Ischebeck, Andreas Krause:
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces. ICML 2019: 3429-3438 - [c2]Johannes Kirschner, Andreas Krause:
Stochastic Bandits with Context Distributions. NeurIPS 2019: 14090-14099 - [i3]Johannes Kirschner, Mojmír Mutný, Nicole Hiller, Rasmus Ischebeck, Andreas Krause:
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces. CoRR abs/1902.03229 (2019) - [i2]Johannes Kirschner, Andreas Krause:
Stochastic Bandits with Context Distributions. CoRR abs/1906.02685 (2019) - 2018
- [c1]Johannes Kirschner, Andreas Krause:
Information Directed Sampling and Bandits with Heteroscedastic Noise. COLT 2018: 358-384 - [i1]Nikolay Nikolov, Johannes Kirschner, Felix Berkenkamp, Andreas Krause:
Information-Directed Exploration for Deep Reinforcement Learning. CoRR abs/1812.07544 (2018)
Coauthor Index
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