| AI safety gridworlds J Leike, M Martic, V Krakovna, PA Ortega, T Everitt, A Lefrancq, L Orseau, ... arXiv preprint arXiv:1711.09883, 2017 | 228 | 2017 |
| Reinforcement Learning with a Corrupted Reward Channel T Everitt, V Krakovna, L Orseau, M Hutter, S Legg IJCAI AI & Autonomy, 2017 | 80 | 2017 |
| Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models V Krakovna, F Doshi-Velez ICML Workshop on Human Interpretability (WHI 2016), arXiv preprint arXiv …, 2016 | 56 | 2016 |
| Reward tampering problems and solutions in reinforcement learning: A causal influence diagram perspective T Everitt, M Hutter, R Kumar, V Krakovna Synthese 198 (27), 6435-6467, 2021 | 35 | 2021 |
| Penalizing side effects using stepwise relative reachability V Krakovna, L Orseau, R Kumar, M Martic, S Legg arXiv preprint arXiv:1806.01186, 2018 | 34 | 2018 |
| Specification gaming: the flip side of AI ingenuity V Krakovna, J Uesato, V Mikulik, M Rahtz, T Everitt, R Kumar, Z Kenton, ... https://deepmind.com/blog/article/Specification-gaming-the-flip-side-of-AI …, 2020 | 32 | 2020 |
| Specification gaming examples in AI V Krakovna tinyurl.com/specification-gaming, 2018 | 30* | 2018 |
| Modeling AGI safety frameworks with causal influence diagrams T Everitt, R Kumar, V Krakovna, S Legg arXiv preprint arXiv:1906.08663, 2019 | 22 | 2019 |
| Avoiding Side Effects By Considering Future Tasks V Krakovna, L Orseau, R Ngo, M Martic, S Legg NeurIPS 2020, arXiv preprint arXiv:2010.07877, 2020 | 20 | 2020 |
| Measuring and avoiding side effects using relative reachability V Krakovna, L Orseau, M Martic, S Legg arXiv preprint arXiv:1806.01186, 2018 | 14 | 2018 |
| REALab: An embedded perspective on tampering R Kumar, J Uesato, R Ngo, T Everitt, V Krakovna, S Legg arXiv preprint arXiv:2011.08820, 2020 | 6 | 2020 |
| Interpretable selection and visualization of features and interactions using bayesian forests V Krakovna, J Du, JS Liu Statistics and its Interface 2018 (Volume 11 Number 3), arXiv preprint arXiv …, 2015 | 6* | 2015 |
| A generalized-zero-preserving method for compact encoding of concept lattices M Skala, V Krakovna, J Kramár, G Penn Proceedings of the 48th annual meeting of the Association for Computational …, 2010 | 6 | 2010 |
| A Minimalistic Approach to Sum-Product Network Learning for Real Applications V Krakovna, M Looks ICLR 2016 workshop, arXiv preprint arXiv:1602.04259, 2016 | 4 | 2016 |
| Avoiding tampering incentives in deep RL via decoupled approval J Uesato, R Kumar, V Krakovna, T Everitt, R Ngo, S Legg arXiv preprint arXiv:2011.08827, 2020 | 3 | 2020 |
| Building interpretable models: From Bayesian networks to neural networks V Krakovna | 3 | 2016 |
Tom EverittSenior researcher at DeepMindVerified email at google.com
