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ORCIDChristoph Dann
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Books and Theses
- 2020
[b1]Christoph Dann:
Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees. Carnegie Mellon University, USA, 2020
Journal Articles
- 2017
- [j4]Amit Adam
, Christoph Dann, Omer Yair, Shai Mazor, Sebastian Nowozin:
Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo. IEEE Trans. Pattern Anal. Mach. Intell. 39(5): 851-864 (2017) - [j3]Markus R. Dann, Christoph Dann:
Automated matching of pipeline corrosion features from in-line inspection data. Reliab. Eng. Syst. Saf. 162: 40-50 (2017) - 2015
- [j2]Alborz Geramifard, Christoph Dann, Robert H. Klein, William Dabney, Jonathan P. How:
RLPy: a value-function-based reinforcement learning framework for education and research. J. Mach. Learn. Res. 16: 1573-1578 (2015) - 2014
- [j1]Christoph Dann, Gerhard Neumann, Jan Peters:
Policy evaluation with temporal differences: a survey and comparison. J. Mach. Learn. Res. 15(1): 809-883 (2014)
Conference and Workshop Papers
- 2024
- [c31]Christoph Dann, Claudio Gentile, Aldo Pacchiano:
Data-Driven Online Model Selection With Regret Guarantees. AISTATS 2024: 1531-1539 - [c30]Gokul Swamy, Christoph Dann, Rahul Kidambi, Steven Wu, Alekh Agarwal:
A Minimaximalist Approach to Reinforcement Learning from Human Feedback. ICML 2024 - 2023
- [c29]Christoph Dann, Mohammad Ghavamzadeh, Teodor V. Marinov:
Multiple-policy High-confidence Policy Evaluation. AISTATS 2023: 9470-9487 - [c28]Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan:
Pseudonorm Approachability and Applications to Regret Minimization. ALT 2023: 471-509 - [c27]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
A Unified Algorithm for Stochastic Path Problems. ALT 2023: 510-557 - [c26]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond. COLT 2023: 5503-5570 - [c25]Christoph Dann, Yishay Mansour, Mehryar Mohri:
Reinforcement Learning Can Be More Efficient with Multiple Rewards. ICML 2023: 6948-6967 - [c24]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
Best of Both Worlds Policy Optimization. ICML 2023: 6968-7008 - [c23]Jonathan Lee, Alekh Agarwal, Christoph Dann, Tong Zhang:
Learning in POMDPs is Sample-Efficient with Hindsight Observability. ICML 2023: 18733-18773 - 2022
- [c22]Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Qiuyi (Richard) Zhang:
Leveraging Initial Hints for Free in Stochastic Linear Bandits. ALT 2022: 282-318 - [c21]Chen-Yu Wei, Christoph Dann, Julian Zimmert:
A Model Selection Approach for Corruption Robust Reinforcement Learning. ALT 2022: 1043-1096 - [c20]Mariya Toneva, Jennifer Williams, Anand Bollu, Christoph Dann, Leila Wehbe:
Same Cause; Different Effects in the Brain. CLeaR 2022: 787-825 - [c19]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. ICML 2022: 4666-4689 - [c18]Aldo Pacchiano, Christoph Dann, Claudio Gentile:
Best of Both Worlds Model Selection. NeurIPS 2022 - 2021
- [c17]Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit:
Dynamic Balancing for Model Selection in Bandits and RL. ICML 2021: 2276-2285 - [c16]Christoph Dann, Teodor Vanislavov Marinov, Mehryar Mohri, Julian Zimmert:
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning. NeurIPS 2021: 1-12 - [c15]Zhilei Wang, Pranjal Awasthi, Christoph Dann, Ayush Sekhari, Claudio Gentile:
Neural Active Learning with Performance Guarantees. NeurIPS 2021: 7510-7521 - [c14]Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert:
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. NeurIPS 2021: 12040-12051 - [c13]Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, Karthik Sridharan:
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. NeurIPS 2021: 19033-19045 - 2020
- [c12]Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill:
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy. AAAI 2020: 4436-4443 - [c11]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Reinforcement Learning with Feedback Graphs. NeurIPS 2020 - 2019
- [c10]Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill:
Policy Certificates: Towards Accountable Reinforcement Learning. ICML 2019: 1507-1516 - 2018
- [c9]Philip S. Thomas, Christoph Dann, Emma Brunskill:
Decoupling Gradient-Like Learning Rules from Representations. ICML 2018: 4924-4932 - [c8]Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
On Oracle-Efficient PAC RL with Rich Observations. NeurIPS 2018: 1429-1439 - 2017
- [c7]Karan Goel, Christoph Dann, Emma Brunskill:
Sample Efficient Policy Search for Optimal Stopping Domains. IJCAI 2017: 1711-1717 - [c6]Christoph Dann, Tor Lattimore, Emma Brunskill:
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning. NIPS 2017: 5713-5723 - 2016
- [c5]Philip S. Thomas, Bruno Castro da Silva, Christoph Dann, Emma Brunskill:
Energetic Natural Gradient Descent. ICML 2016: 2887-2895 - 2015
- [c4]Christoph Dann, Gerhard Neumann, Jan Peters:
Policy Evaluation with Temporal Differences: A Survey and Comparison (Extended Abstract). ICAPS 2015: 359-360 - [c3]Christoph Dann, Emma Brunskill:
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning. NIPS 2015: 2818-2826 - [c2]Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas, Eric P. Xing:
The Human Kernel. NIPS 2015: 2854-2862 - 2012
- [c1]Christoph Dann, Peter V. Gehler, Stefan Roth, Sebastian Nowozin:
Pottics - The Potts Topic Model for Semantic Image Segmentation. DAGM/OAGM Symposium 2012: 397-407
Informal and Other Publications
- 2024
- [i31]Gokul Swamy, Christoph Dann, Rahul Kidambi, Zhiwei Steven Wu, Alekh Agarwal:
A Minimaximalist Approach to Reinforcement Learning from Human Feedback. CoRR abs/2401.04056 (2024) - [i30]Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan:
Rate-Preserving Reductions for Blackwell Approachability. CoRR abs/2406.07585 (2024) - [i29]Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Avinava Dubey, Alexandre Ramé, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Léonard Hussenot, Olivier Bachem, Edouard Leurent:
Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning. CoRR abs/2407.15762 (2024) - 2023
- [i28]Jonathan N. Lee, Alekh Agarwal, Christoph Dann, Tong Zhang:
Learning in POMDPs is Sample-Efficient with Hindsight Observability. CoRR abs/2301.13857 (2023) - [i27]Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan:
Pseudonorm Approachability and Applications to Regret Minimization. CoRR abs/2302.01517 (2023) - [i26]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
Best of Both Worlds Policy Optimization. CoRR abs/2302.09408 (2023) - [i25]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond. CoRR abs/2302.09739 (2023) - [i24]Aldo Pacchiano, Christoph Dann, Claudio Gentile:
Data-Driven Regret Balancing for Online Model Selection in Bandits. CoRR abs/2306.02869 (2023) - 2022
- [i23]Mariya Toneva, Jennifer Williams, Anand Bollu, Christoph Dann, Leila Wehbe:
Same Cause; Different Effects in the Brain. CoRR abs/2202.10376 (2022) - [i22]Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Qiuyi (Richard) Zhang:
Leveraging Initial Hints for Free in Stochastic Linear Bandits. CoRR abs/2203.04274 (2022) - [i21]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. CoRR abs/2206.09421 (2022) - [i20]Aldo Pacchiano, Christoph Dann, Claudio Gentile:
Best of Both Worlds Model Selection. CoRR abs/2206.14912 (2022) - [i19]Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert:
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. CoRR abs/2208.10904 (2022) - [i18]Christoph Dann, Chen-Yu Wei, Julian Zimmert:
A Unified Algorithm for Stochastic Path Problems. CoRR abs/2210.09255 (2022) - 2021
- [i17]Pranjal Awasthi, Christoph Dann, Claudio Gentile, Ayush Sekhari, Zhilei Wang:
Neural Active Learning with Performance Guarantees. CoRR abs/2106.03243 (2021) - [i16]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. CoRR abs/2106.11519 (2021) - [i15]Christoph Dann, Teodor V. Marinov, Mehryar Mohri, Julian Zimmert:
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning. CoRR abs/2107.01264 (2021) - [i14]Chen-Yu Wei, Christoph Dann, Julian Zimmert:
A Model Selection Approach for Corruption Robust Reinforcement Learning. CoRR abs/2110.03580 (2021) - 2020
- [i13]Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan:
Reinforcement Learning with Feedback Graphs. CoRR abs/2005.03789 (2020) - [i12]Aldo Pacchiano, Christoph Dann, Claudio Gentile, Peter L. Bartlett:
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL. CoRR abs/2012.13045 (2020) - 2019
- [i11]Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill:
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy. CoRR abs/1911.01546 (2019) - 2018
- [i10]Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire:
On Polynomial Time PAC Reinforcement Learning with Rich Observations. CoRR abs/1803.00606 (2018) - [i9]Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill:
Policy Certificates: Towards Accountable Reinforcement Learning. CoRR abs/1811.03056 (2018) - 2017
- [i8]Karan Goel, Christoph Dann, Emma Brunskill:
Sample Efficient Policy Search for Optimal Stopping Domains. CoRR abs/1702.06238 (2017) - [i7]Christoph Dann, Tor Lattimore, Emma Brunskill:
UBEV - A More Practical Algorithm for Episodic RL with Near-Optimal PAC and Regret Guarantees. CoRR abs/1703.07710 (2017) - [i6]Philip S. Thomas, Christoph Dann, Emma Brunskill:
Decoupling Learning Rules from Representations. CoRR abs/1706.03100 (2017) - 2016
- [i5]Christoph Dann, Katja Hofmann, Sebastian Nowozin:
Memory Lens: How Much Memory Does an Agent Use? CoRR abs/1611.06928 (2016) - 2015
- [i4]Amit Adam, Christoph Dann, Omer Yair, Shai Mazor, Sebastian Nowozin:
Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo. CoRR abs/1507.06173 (2015) - [i3]Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas, Eric P. Xing:
The Human Kernel. CoRR abs/1510.07389 (2015) - [i2]Christoph Dann, Emma Brunskill:
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning. CoRR abs/1510.08906 (2015) - [i1]Andrew Gordon Wilson, Christoph Dann, Hannes Nickisch:
Thoughts on Massively Scalable Gaussian Processes. CoRR abs/1511.01870 (2015)
Coauthor Index
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