Pottics - The Potts Topic Model for Semantic Image Segmentation. - Christoph Dann; Peter V. Gehler; Stefan Roth 001; Sebastian Nowozin (DAGM/OAGM Symposium, 2012)

Policy evaluation with temporal differences: a survey and comparison. - Christoph Dann; Gerhard Neumann; Jan Peters 001 (J. Mach. Learn. Res., 2014)

Policy Evaluation with Temporal Differences: A Survey and Comparison (Extended Abstract). - Christoph Dann; Gerhard Neumann; Jan Peters 001 (ICAPS, 2015)

Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning. - Christoph Dann; Emma Brunskill (NIPS, 2015)

The Human Kernel. - Andrew Gordon Wilson; Christoph Dann; Christopher G. Lucas; Eric P. Xing (NIPS, 2015)

Thoughts on Massively Scalable Gaussian Processes. - Andrew Gordon Wilson; Christoph Dann; Hannes Nickisch (CoRR, 2015)

RLPy: a value-function-based reinforcement learning framework for education and research. - Alborz Geramifard; Christoph Dann; Robert H. Klein; William Dabney; Jonathan P. How (J. Mach. Learn. Res., 2015)

Energetic Natural Gradient Descent. - Philip S. Thomas; Bruno Castro da Silva; Christoph Dann; Emma Brunskill (ICML, 2016)

Memory Lens: How Much Memory Does an Agent Use? - Christoph Dann; Katja Hofmann; Sebastian Nowozin (CoRR, 2016)

Sample Efficient Policy Search for Optimal Stopping Domains. - Karan Goel; Christoph Dann; Emma Brunskill (IJCAI, 2017)

Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning. - Christoph Dann; Tor Lattimore; Emma Brunskill (NIPS, 2017)

UBEV - A More Practical Algorithm for Episodic RL with Near-Optimal PAC and Regret Guarantees. - Christoph Dann; Tor Lattimore; Emma Brunskill (CoRR, 2017)

Decoupling Learning Rules from Representations. - Philip S. Thomas; Christoph Dann; Emma Brunskill (CoRR, 2017)

Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo. - Amit Adam; Christoph Dann; Omer Yair; Shai Mazor; Sebastian Nowozin (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

Automated matching of pipeline corrosion features from in-line inspection data. - Markus R. Dann; Christoph Dann (Reliab. Eng. Syst. Saf., 2017)

Decoupling Gradient-Like Learning Rules from Representations. - Philip S. Thomas; Christoph Dann; Emma Brunskill (ICML, 2018)

On Oracle-Efficient PAC RL with Rich Observations. - Christoph Dann; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001; Robert E. Schapire (NeurIPS, 2018)

On Polynomial Time PAC Reinforcement Learning with Rich Observations. - Christoph Dann; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001; Robert E. Schapire (CoRR, 2018)

Policy Certificates: Towards Accountable Reinforcement Learning. - Christoph Dann; Lihong Li 001; Wei Wei; Emma Brunskill (ICML, 2019)

Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy. - Ramtin Keramati; Christoph Dann; Alex Tamkin; Emma Brunskill (AAAI, 2020)

Reinforcement Learning with Feedback Graphs. - Christoph Dann; Yishay Mansour; Mehryar Mohri; Ayush Sekhari; Karthik Sridharan (NeurIPS, 2020)

Regret Bound Balancing and Elimination for Model Selection in Bandits and RL. - Aldo Pacchiano; Christoph Dann; Claudio Gentile; Peter L. Bartlett (CoRR, 2020)

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees. - Christoph Dann (2020)

Dynamic Balancing for Model Selection in Bandits and RL. - Ashok Cutkosky; Christoph Dann; Abhimanyu Das; Claudio Gentile; Aldo Pacchiano; Manish Purohit (ICML, 2021)

Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning. - Christoph Dann; Teodor Vanislavov Marinov; Mehryar Mohri; Julian Zimmert (NeurIPS, 2021)

A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning. - Christoph Dann; Mehryar Mohri; Tong Zhang 001; Julian Zimmert (NeurIPS, 2021)

Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations. - Ayush Sekhari; Christoph Dann; Mehryar Mohri; Yishay Mansour; Karthik Sridharan (NeurIPS, 2021)

Neural Active Learning with Performance Guarantees. - Zhilei Wang; Pranjal Awasthi; Christoph Dann; Ayush Sekhari; Claudio Gentile (NeurIPS, 2021)

Leveraging Initial Hints for Free in Stochastic Linear Bandits. - Ashok Cutkosky; Christoph Dann; Abhimanyu Das; Qiuyi (Richard) Zhang (ALT, 2022)

A Model Selection Approach for Corruption Robust Reinforcement Learning. - Chen-Yu Wei; Christoph Dann; Julian Zimmert (ALT, 2022)

Same Cause; Different Effects in the Brain. - Mariya Toneva; Jennifer Williams; Anand Bollu; Christoph Dann; Leila Wehbe (CLeaR, 2022)

Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation. - Christoph Dann; Yishay Mansour; Mehryar Mohri; Ayush Sekhari; Karthik Sridharan (ICML, 2022)

Best of Both Worlds Model Selection. - Aldo Pacchiano; Christoph Dann; Claudio Gentile (NeurIPS, 2022)

Multiple-policy High-confidence Policy Evaluation. - Christoph Dann; Mohammad Ghavamzadeh; Teodor V. Marinov (AISTATS, 2023)

Pseudonorm Approachability and Applications to Regret Minimization. - Christoph Dann; Yishay Mansour; Mehryar Mohri; Jon Schneider; Balasubramanian Sivan (ALT, 2023)

A Unified Algorithm for Stochastic Path Problems. - Christoph Dann; Chen-Yu Wei; Julian Zimmert (ALT, 2023)

A Blackbox Approach to Best of Both Worlds in Bandits and Beyond. - Christoph Dann; Chen-Yu Wei; Julian Zimmert (COLT, 2023)

Learning in POMDPs is Sample-Efficient with Hindsight Observability. - Jonathan Lee 002; Alekh Agarwal; Christoph Dann; Tong Zhang 001 (ICML, 2023)

Reinforcement Learning Can Be More Efficient with Multiple Rewards. - Christoph Dann; Yishay Mansour; Mehryar Mohri (ICML, 2023)

Best of Both Worlds Policy Optimization. - Christoph Dann; Chen-Yu Wei; Julian Zimmert (ICML, 2023)

Data-Driven Regret Balancing for Online Model Selection in Bandits. - Aldo Pacchiano; Christoph Dann; Claudio Gentile (CoRR, 2023)

Data-Driven Online Model Selection With Regret Guarantees. - Christoph Dann; Claudio Gentile; Aldo Pacchiano (AISTATS, 2024)

Conditional Language Policy: A General Framework For Steerable Multi-Objective Finetuning. - Kaiwen Wang; Rahul Kidambi; Ryan Sullivan; Alekh Agarwal; Christoph Dann; Andrea Michi; Marco Gelmi; Yunxuan Li; Raghav Gupta; Avinava Dubey; Alexandre Ramé; Johan Ferret; et al. (EMNLP (Findings), 2024)

A Minimaximalist Approach to Reinforcement Learning from Human Feedback. - Gokul Swamy; Christoph Dann; Rahul Kidambi; Steven Wu 001; Alekh Agarwal (ICML, 2024)

Conditioned Language Policy: A General Framework for Steerable Multi-Objective Finetuning. - Kaiwen Wang; Rahul Kidambi; Ryan Sullivan; Alekh Agarwal; Christoph Dann; Andrea Michi; Marco Gelmi; Yunxuan Li; Raghav Gupta; Avinava Dubey; Alexandre Ramé; Johan Ferret; et al. (CoRR, 2024)

Preserving Expert-Level Privacy in Offline Reinforcement Learning. - Navodita Sharma; Vishnu Vinod; Abhradeep Thakurta; Alekh Agarwal; Borja Balle; Christoph Dann; Aravindan Raghuveer (CoRR, 2024)

Rate-Preserving Reductions for Blackwell Approachability. - Christoph Dann; Yishay Mansour; Mehryar Mohri; Jon Schneider; Balasubramanian Sivan (COLT, 2025)

Design Considerations in Offline Preference-based RL. - Alekh Agarwal; Christoph Dann; Teodor V. Marinov (CoRR, 2025)

Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective. - Jiawei Huang; Bingcong Li; Christoph Dann; Niao He (CoRR, 2025)

Mitigating Preference Hacking in Policy Optimization with Pessimism. - Dhawal Gupta; Adam Fisch; Christoph Dann; Alekh Agarwal (CoRR, 2025)