Christoph Dann
Christoph Dann
Research Scientist, Google
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Zitiert von
Zitiert von
Policy evaluation with temporal differences: a survey and comparison.
C Dann, G Neumann, J Peters
Journal of Machine Learning Research 15 (1), 809-883, 2014
Sample complexity of episodic fixed-horizon reinforcement learning
C Dann, E Brunskill
Advances in Neural Information Processing Systems, 2818-2826, 2015
Unifying PAC and regret: Uniform PAC bounds for episodic reinforcement learning
C Dann, T Lattimore, E Brunskill
Advances in Neural Information Processing Systems, 5717-5727, 2017
RLPy: a value-function-based reinforcement learning framework for education and research.
A Geramifard, C Dann, RH Klein, W Dabney, JP How
Journal of Machine Learning Research 16, 1573-1578, 2015
Thoughts on massively scalable Gaussian processes
AG Wilson, C Dann, H Nickisch
arXiv preprint arXiv:1511.01870, 2015
Policy Certificates: Towards Accountable Reinforcement Learning
C Dann, L Li, W Wei, E Brunskill
arXiv preprint arXiv:1811.03056, 2018
The human kernel
AG Wilson, C Dann, C Lucas, EP Xing
Advances in Neural Information Processing Systems, 2854-2862, 2015
On Oracle-Efficient PAC RL with Rich Observations
C Dann, N Jiang, A Krishnamurthy, A Agarwal, J Langford, RE Schapire
Advances in Neural Information Processing Systems, 1429-1439, 2018
Bayesian time-of-flight for realtime shape, illumination and albedo
A Adam, C Dann, O Yair, S Mazor, S Nowozin
IEEE transactions on pattern analysis and machine intelligence 39 (5), 851-864, 2017
Automated matching of pipeline corrosion features from in-line inspection data
MR Dann, C Dann
Reliability Engineering & System Safety 162, 40-50, 2017
Pottics–the potts topic model for semantic image segmentation
C Dann, P Gehler, S Roth, S Nowozin
Joint DAGM (German Association for Pattern Recognition) and OAGM Symposium …, 2012
Energetic natural gradient descent
P Thomas, BC Silva, C Dann, E Brunskill
International Conference on Machine Learning, 2887-2895, 2016
Sample Efficient Policy Search for Optimal Stopping Domains
K Goel, C Dann, E Brunskill
arXiv preprint arXiv:1702.06238, 2017
Off-policy learning combined with automatic feature expansion for solving large MDPs
A Geramifard, C Dann, JP How
Proc. 1st Multidisciplinary Conf. on Reinforcement Learning and Decision …, 2013
Decoupling Gradient-Like Learning Rules from Representations
P Thomas, C Dann, E Brunskill
International Conference on Machine Learning, 4924-4932, 2018
Time-of-flight simulation of multipath light phenomena
S Nowozin, A Adam, C Dann
US Patent App. 14/692,527, 2016
Scaling up behavioral science interventions in online education
RF Kizilcec, J Reich, M Yeomans, C Dann, E Brunskill, G Lopez, S Turkay, ...
Proceedings of the National Academy of Sciences, 2020
Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy
R Keramati, C Dann, A Tamkin, E Brunskill
arXiv preprint arXiv:1911.01546, 2019
Memory Lens: How Much Memory Does an Agent Use?
C Dann, K Hofmann, S Nowozin
arXiv preprint arXiv:1611.06928, 2016
Reinforcement Learning with Feedback Graphs
C Dann, Y Mansour, M Mohri, A Sekhari, K Sridharan
arXiv preprint arXiv:2005.03789, 2020
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