| Deep reinforcement learning from human preferences PF Christiano, J Leike, T Brown, M Martic, S Legg, D Amodei Advances in Neural Information Processing Systems 30, 4299-4307, 2017 | 783 | 2017 |
| Evaluating large language models trained on code M Chen, J Tworek, H Jun, Q Yuan, HPO Pinto, J Kaplan, H Edwards, ... arXiv preprint arXiv:2107.03374, 2021 | 368 | 2021 |
| AI Safety Gridworlds J Leike, M Martic, V Krakovna, PA Ortega, T Everitt, A Lefrancq, L Orseau, ... arXiv preprint arXiv:1711.09883, 2017 | 262 | 2017 |
| Training language models to follow instructions with human feedback L Ouyang, J Wu, X Jiang, D Almeida, CL Wainwright, P Mishkin, C Zhang, ... arXiv preprint arXiv:2203.02155, 2022 | 217 | 2022 |
| Reward learning from human preferences and demonstrations in Atari B Ibarz, J Leike, T Pohlen, G Irving, S Legg, D Amodei Advances in Neural Information Processing Systems, 8011-8023, 2018 | 173 | 2018 |
| Learning to Understand Goal Specifications by Modelling Reward D Bahdanau, F Hill, J Leike, E Hughes, P Kohli, E Grefenstette arXiv preprint arXiv:1806.01946, 2018 | 142* | 2018 |
| Scalable agent alignment via reward modeling: a research direction J Leike, D Krueger, T Everitt, M Martic, V Maini, S Legg arXiv preprint arXiv:1811.07871, 2018 | 137 | 2018 |
| Ranking Templates for Linear Loops J Leike, M Heizmann Logical Methods in Computer Science, 2015 | 86* | 2015 |
| Linear ranking for linear lasso programs M Heizmann, J Hoenicke, J Leike, A Podelski Automated Technology for Verification and Analysis, 365-380, 2013 | 56 | 2013 |
| Learning human objectives by evaluating hypothetical behavior S Reddy, A Dragan, S Levine, S Legg, J Leike International Conference on Machine Learning, 8020-8029, 2020 | 52 | 2020 |
| Recursively summarizing books with human feedback J Wu, L Ouyang, DM Ziegler, N Stiennon, R Lowe, J Leike, P Christiano arXiv preprint arXiv:2109.10862, 2021 | 49 | 2021 |
| Geometric nontermination arguments J Leike, M Heizmann International Conference on Tools and Algorithms for the Construction and …, 2018 | 42* | 2018 |
| Thompson sampling is asymptotically optimal in general environments J Leike, T Lattimore, L Orseau, M Hutter Conference on Uncertainty in Artificial Intelligence, 2016 | 37 | 2016 |
| Bad universal priors and notions of optimality J Leike, M Hutter Conference on Learning Theory, 1244-1259, 2015 | 37 | 2015 |
| Ultimate Automizer with array interpolation M Heizmann, D Dietsch, J Leike, B Musa, A Podelski International Conference on Tools and Algorithms for the Construction and …, 2015 | 32* | 2015 |
| Quantifying Differences in Reward Functions A Gleave, M Dennis, S Legg, S Russell, J Leike arXiv preprint arXiv:2006.13900, 2020 | 31 | 2020 |
| Ultimate automizer with two-track proofs M Heizmann, D Dietsch, M Greitschus, J Leike, B Musa, C Schätzle, ... International Conference on Tools and Algorithms for the Construction and …, 2016 | 30* | 2016 |
| Institutionalizing ethics in AI through broader impact requirements CEA Prunkl, C Ashurst, M Anderljung, H Webb, J Leike, A Dafoe Nature Machine Intelligence 3 (2), 104-110, 2021 | 23 | 2021 |
| Universal Reinforcement Learning Algorithms: Survey and Experiments J Aslanides, J Leike, M Hutter arXiv preprint arXiv:1705.10557, 2017 | 23 | 2017 |
| Nonparametric general reinforcement learning J Leike PhD thesis, Australian National University, 2016 | 21 | 2016 |
Marcus HutterResearcher@DeepMind & Professor at ANUVerified email at anu.edu.au
