| 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 | 146 | 2014 |
| Sample complexity of episodic fixed-horizon reinforcement learning C Dann, E Brunskill Advances in Neural Information Processing Systems, 2818-2826, 2015 | 110 | 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 | 89 | 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 | 59* | 2015 |
| Thoughts on massively scalable Gaussian processes AG Wilson, C Dann, H Nickisch arXiv preprint arXiv:1511.01870, 2015 | 57 | 2015 |
| Policy Certificates: Towards Accountable Reinforcement Learning C Dann, L Li, W Wei, E Brunskill arXiv preprint arXiv:1811.03056, 2018 | 45 | 2018 |
| The human kernel AG Wilson, C Dann, C Lucas, EP Xing Advances in Neural Information Processing Systems, 2854-2862, 2015 | 45 | 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 | 30* | 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 | 23 | 2017 |
| Automated matching of pipeline corrosion features from in-line inspection data MR Dann, C Dann Reliability Engineering & System Safety 162, 40-50, 2017 | 16 | 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 | 13 | 2012 |
| Energetic natural gradient descent P Thomas, BC Silva, C Dann, E Brunskill International Conference on Machine Learning, 2887-2895, 2016 | 12 | 2016 |
| Sample Efficient Policy Search for Optimal Stopping Domains K Goel, C Dann, E Brunskill arXiv preprint arXiv:1702.06238, 2017 | 6 | 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 | 6 | 2013 |
| Decoupling Gradient-Like Learning Rules from Representations P Thomas, C Dann, E Brunskill International Conference on Machine Learning, 4924-4932, 2018 | 5 | 2018 |
| Time-of-flight simulation of multipath light phenomena S Nowozin, A Adam, C Dann US Patent App. 14/692,527, 2016 | 4 | 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 | 3 | 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 | 3 | 2019 |
| Memory Lens: How Much Memory Does an Agent Use? C Dann, K Hofmann, S Nowozin arXiv preprint arXiv:1611.06928, 2016 | 3 | 2016 |
| Reinforcement Learning with Feedback Graphs C Dann, Y Mansour, M Mohri, A Sekhari, K Sridharan arXiv preprint arXiv:2005.03789, 2020 | | 2020 |
Emma BrunskillAssistant Professor of Computer Science, Stanford UniversityBestätigte E-Mail-Adresse bei cs.stanford.edu
