| AI safety gridworlds J Leike, M Martic, V Krakovna, PA Ortega, T Everitt, A Lefrancq, L Orseau, ... arXiv preprint arXiv:1711.09883, 2017 | 162 | 2017 |
| Reinforcement Learning with Corrupted Reward Channel T Everitt, V Krakovna, L Orseau, M Hutter, S Legg 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017 | 55 | 2017 |
| AGI safety literature review T Everitt, G Lea, M Hutter arXiv preprint arXiv:1805.01109, 2018 | 53 | 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 | 50 | 2018 |
| Count-based exploration in feature space for reinforcement learning J Martin, SN Sasikumar, T Everitt, M Hutter arXiv preprint arXiv:1706.08090, 2017 | 45 | 2017 |
| Avoiding wireheading with value reinforcement learning T Everitt, M Hutter International Conference on Artificial General Intelligence, 12-22, 2016 | 30 | 2016 |
| Self-modification of policy and utility function in rational agents T Everitt, D Filan, M Daswani, M Hutter International Conference on Artificial General Intelligence, 1-11, 2016 | 25 | 2016 |
| Towards safe artificial general intelligence T Everitt The Australian National University, 2018 | 17 | 2018 |
| Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings T Everitt, PA Ortega, E Barnes, S Legg arXiv preprint arXiv:1902.09980, 2019 | 16 | 2019 |
| Universal artificial intelligence T Everitt, M Hutter Foundations of Trusted Autonomy, 15-46, 2018 | 14 | 2018 |
| Analytical results on the BFS vs. DFS algorithm selection problem. Part I: tree search T Everitt, M Hutter Australasian Joint Conference on Artificial Intelligence, 157-165, 2015 | 12 | 2015 |
| Death and suicide in universal artificial intelligence J Martin, T Everitt, M Hutter International Conference on Artificial General Intelligence, 23-32, 2016 | 11 | 2016 |
| Free lunch for optimisation under the universal distribution T Everitt, T Lattimore, M Hutter 2014 IEEE Congress on Evolutionary Computation (CEC), 167-174, 2014 | 11 | 2014 |
| Sequential extensions of causal and evidential decision theory T Everitt, J Leike, M Hutter International Conference on Algorithmic DecisionTheory, 205-221, 2015 | 10 | 2015 |
| Reward tampering problems and solutions in reinforcement learning: A causal influence diagram perspective T Everitt, M Hutter arXiv preprint arXiv:1908.04734, 2019 | 9 | 2019 |
| Modeling AGI safety frameworks with causal influence diagrams T Everitt, R Kumar, V Krakovna, S Legg arXiv preprint arXiv:1906.08663, 2019 | 9 | 2019 |
| Artificial general intelligence T Everitt, B Goertzel, A Potapov Lecture Notes in Artificial Intelligence. Heidelberg: Springer, 2017 | 9 | 2017 |
| The alignment problem for Bayesian history-based reinforcement learners T Everitt, M Hutter Under submission, 2018 | 8 | 2018 |
| Can we measure the difficulty of an optimization problem? T Alpcan, T Everitt, M Hutter 2014 IEEE Information Theory Workshop (ITW 2014), 356-360, 2014 | 8 | 2014 |
| Analytical Results on the BFS vs. DFS Algorithm Selection Problem: Part II: Graph Search T Everitt, M Hutter Australasian Joint Conference on Artificial Intelligence, 166-178, 2015 | 7 | 2015 |
Marcus HutterResearcher@DeepMind & Professor at ANUVerified email at anu.edu.au
