| Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback S Casper*, X Davies*, C Shi, TK Gilbert, J Scheurer, J Rando, ... TMLR, 2023 | 150 | 2023 |
| Defining and Characterizing Reward Hacking J Skalse*, NHR Howe, D Krasheninnikov, D Krueger* Advances in Neural Information Processing Systems 36, 2022 | 100 | 2022 |
| Preferences Implicit in the State of the World R Shah*, D Krasheninnikov*, J Alexander, P Abbeel, A Dragan International Conference on Learning Representations, 2019 | 78* | 2019 |
| Harms from Increasingly Agentic Algorithmic Systems A Chan, R Salganik, A Markelius, C Pang, N Rajkumar, D Krasheninnikov, ... Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023 | 39* | 2023 |
| Benefits of Assistance over Reward Learning R Shah, P Freire, N Alex, R Freedman, D Krasheninnikov, L Chan, ... NeurIPS Workshop on Cooperative AI, best paper award, 2020 | 22 | 2020 |
| Assistance with large language models D Krasheninnikov*, E Krasheninnikov*, D Krueger NeurIPS ML Safety Workshop, 2022 | 9 | 2022 |
| Meta-(out-of-context) learning in neural networks D Krasheninnikov*, E Krasheninnikov*, B Mlodozeniec, D Krueger arXiv preprint arXiv:2310.15047, 2023 | 4* | 2023 |
| Combining reward information from multiple sources D Krasheninnikov, R Shah, H van Hoof NeurIPS Workshop on Learning with Rich Experience, 2019 | 3 | 2019 |
David Scott KruegerUniversity Assistant Professor, University of CambridgeVerified email at cam.ac.uk
