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Christoph Dann
Christoph Dann
Welcome! I am a Research Scientist at Google Research in Zurich. My research focuses on fundamental questions in reinforcement learning. Before, I obtained my PhD in the Machine Learning Department at Carnegie Mellon University, where I was advised by Emma Brunskill.
- Email: cdann@cdann.net
- Github: chrodan
- Google Scholar
- Curriculum Vitae
News
- April 2022: Our paper A Model Selection Approach for Corruption Robust Reinforcement Learning won the best paper award at ALT 2022.
- October 2019: I am excited to join Google Research as a Research Scientist!
- September 2019: After 5 fabulous years in the machine learning department at CMU, I successfully defended my PhD at CMU.
- Summer 2018: I am interning at Google Cloud AI Research, working with Lihong Li and Wei Wei.
- December 2017: I was excited to present our work on uniform PAC, a new learning framework that combines the advantages of the existing PAC and regret frameworks, as a spotlight and poster at NeurIPS. I also got a best reviewer award at NeurIPS17.
- September 2017: Together with my teammates Mariya Toneva, Feier Li and Daniel Dong, I participated in the Citadel Datathon at CMU winning a runner-up cash prize of $2500.
- September 2017: I interned this summer at Microsoft Research New York working with Nan Jian, Akshay Krishnamurthy, Alekh Agarwal, John Langford and Robert Shapire.
- September 2016: I worked this summer with Sebastian Nowozin and Katja Hofmann at Microsoft Resarch Cambridge, UK, on quantifying and estimating the amount of information reinforcement learning agents use from previous observations when they take action in non-Markovian environments.
- November 2015: I received the Datenlotsenpreis 2015 for my master's thesis, an award for outstanding bachelor's and master's theses at TU Darmstadt in the area of Mathematics, Computer Science and Engineering (some info in German here and here)
Publications and Preprints
-
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, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Léonard Hussenot, Olivier Bachem, Edouard Leurent
[Arxiv] -
Rate-Preserving Reductions for Blackwell Approachability
Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan
[Arxiv] -
A Minimaximalist Approach to Reinforcement Learning from Human Feedback
Gokul Swamy, Christoph Dann, Rahul Kidambi, Zhiwei Steven Wu, Alekh Agarwal
ICML '24 [Arxiv] -
Data-Driven Online Model Selection With Regret Guarantees
Aldo Pacchiano, Christoph Dann, Claudio Gentile
AISTATS '24 [Arxiv] -
A Blackbox Approach to Best of Both Worlds in Bandits and Beyond
Christoph Dann, Chen-Yu Wei, Julian Zimmert
COLT '23 [Arxiv] -
Learning in POMDPs is Sample-Efficient with Hindsight Observability
Jonathan N. Lee, Alekh Agarwal, Christoph Dann, Tong Zhang
ICML '23 [Arxiv] -
Reinforcement Learning Can Be More Efficient with Multiple Rewards
Christoph Dann, Yishay Mansour, Mehryar Mohri
ICML '23 [pdf soon] -
Best of Both Worlds Policy Optimization
Christoph Dann, Chen-Yu Wei, Julian Zimmert
ICML '23 short live talk [Arxiv] -
Multiple-policy High-confidence Policy Evaluation
Christoph Dann, Mohammad Ghavamzadeh, Teodor V. Marinov
AISTATS '23 [pdf] -
Pseudonorm Approachability and Applications to Regret Minimization
Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan
ALT '23 [Arxiv] -
A Unified Algorithm for Stochastic Path Problems
Christoph Dann, Chen-Yu Wei, Julian Zimmert
ALT '23 [Arxiv] -
Best of Both Worlds Model Selection
Aldo Pacchiano, Christoph Dann, Claudio Gentile
NeurIPS '22 [Arxiv] -
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
ICML '22 [Arxiv] -
A Model Selection Approach for Corruption Robust Reinforcement Learning
Chen-Yu Wei, Christoph Dann, Julian Zimmert
ALT '22 best paper award [Arxiv] -
Leveraging Initial Hints for Free in Stochastic Linear Bandits
Richard Zhang, Abhimanyu Das, Ashok Cutkosky, Christoph Dann
ALT '22 [Arxiv] -
Same Cause; Different Effect in the Brain
Mariya Toneva, Jennifer Williams, Anand Bollu, Christoph Dann, Leila Wehbe
CLeaR '22 [Arxiv] -
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
NeurIPS '21 with spotlight talk [Arxiv] -
Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning
Christoph Dann, Teodor V. Marinov, Mehryar Mohri, Julian Zimmert
NeurIPS '21 with spotlight talk [Arxiv] -
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning
Christoph Dann, Mehryar Mohri, Tong Zhang, Julian Zimmert
NeurIPS '21 [Arxiv] -
Neural Active Learning with Performance Guarantees
Pranjal Awasthi, Christoph Dann, Claudio Gentile, Ayush Sekhari, Zhilei Wang
NeurIPS '21 [Arxiv] -
Dynamic Balancing for Model Selection in Bandits and RL
Ashok Cutkosky, Christoph Dann, Abhimanyu Das, Claudio Gentile, Aldo Pacchiano, Manish Purohit
ICML '21 [pdf]
This paper is based on this earlier report:
Regret Bound Balancing and Elimination for Model Selection in Bandits and RL
Aldo Pacchiano, Christoph Dann, Claudio Gentile, Peter Bartlett, 2020
[Arxiv] -
Reinforcement Learning with Feedback Graphs
Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan
NeurIPS '20 [pdf] [Arxiv] -
Scaling up behavioral science interventions in online education
René F. Kizilcec, Justin Reich, Michael Yeomans, Christoph Dann, Emma Brunskill, Glenn Lopez, Selen Turkay, Joseph Jay Williams, Dustin Tingley
PNAS '20 [pdf] -
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy
Ramtin Keramati, Christoph Dann, Alex Tamkin, Emma Brunskill
AAAI '20 [Arxiv] -
Policy Certificates: Towards Accountable Reinforcement Learning
Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill
ICML '19 [Arxiv]Also presented at the Workshop on Ethical, Social and Governance Issues in AI at NeurIPS 18 -
On Oracle-Efficient PAC Reinforcement Learning with Rich Observations
Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert Schapire
NeurIPS '18 with spotlight talk [Arxiv]
Previously presented at 12th Annual Machine Learning Symposium at NYAS -
Decoupling Gradient-Like Learning Rules from Representations
Philip S. Thomas, Christoph Dann, Emma Brunskill
ICML '18 [Arxiv] -
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning
Christoph Dann, Tor Lattimore, Emma Brunskill
NeurIPS '17 with spotlight talk [pdf] [Arxiv] [code]Previous version of this paper with stronger focus on linear state space dependency in PAC bound:
UBEV - A More Practical Algorithm for Episodic RL with Near-Optimal PAC and Regret Guarantees
Christoph Dann, Tor Lattimore, Emma Brunskill, March 2017
[Arxiv] -
Sample Efficient Policy Search for Optimal Stopping Domains
Karan Goel, Christoph Dann, Emma Brunskill
IJCAI '17 [Arxiv] -
Automated Matching of Pipeline Corrosion Features from In-line Inspection Data
Markus Dann, Christoph Dann
Reliability Engineering and System Safety '17' [pdf] -
Memory Lens - How Much Memory Does an Agent Use?
Christoph Dann, Katja Hofmann, Sebastian Nowozin
EWRL' 16 [pdf] [Arxiv]Also presented at the Interpretable ML for Complex Systems Workshop at NeurIPS '16 -
Energetic Natural Gradient Descent
Philip S. Thomas, Bruno Castro da Silva, Christoph Dann, Emma Brunskill
ICML '16 [pdf] -
Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo
Amit Adam, Christoph Dann, Omer Yair, Shai Mazor, Sebastian Nowozin
PAMI '16 [pdf] [pdf supp] [video] [publisher pdf] [Arxiv] -
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning
Christoph Dann, Emma Brunskill
NeurIPS '15 [Arxiv] -
The Human Kernel
Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas, Eric P. Xing
NeurIPS '15 [pdf] [Arxiv] -
Thoughts on Massively Scalable Gaussian Processes
Andrew Gordon Wilson, Christoph Dann, Hannes Nickisch, 2015
[Arix] -
RLPy: A Value-Function-Based Reinforcement Learning Framework for Education and Research
Alborz Geramifard, Christoph Dann, Robert H. Klein, William Dabney, Jonathan P. How
JMLR MLOSS Track '15' [pdf] [code] -
Policy Evaluation with Temporal Differences: A Survey and Comparison
Christoph Dann, Gerhard Neumann, Jan Peters
JMLR '14 [pdf] [N-link pendulum linearization derivation] [code]
Also presented at ICAPS 2015 [pdf] -
Off-Policy Learning Combined with Automatic Feature Expansion for Solving Large MDPs
Alborz Geramifard, Christoph Dann, Jonathan P. How
RLDM '13 [pdf] [poster] -
Pottics - The Potts Topic Model for Semantic Image Segmentation
Christoph Dann, Peter Gehler, Stefan Roth, Sebastian Nowozin
DAGM '12 [pdf] [publisher pdf] [poster] [code]
Misc
- Some conferences and journals (for example AAAI and JMLR) want to have clean single-file tex-sources of your papers. A small script of mine, texclean.py can automatically clean-up and merge your tex-sources.