| Behaviour suite for reinforcement learning I Osband, Y Doron, M Hessel, J Aslanides, E Sezener, A Saraiva, ... International Conference on Learning Representations (ICLR), 2019 | 22 | 2019 |
| Minimal sign representation of Boolean functions: algorithms and exact results for low dimensions E Sezener, E Oztop Neural Computation 27 (8), 1796-1823, 2015 | 7 | 2015 |
| Inferring human values for Safe AGI design E Sezener Artificial General Intelligence (AGI) 9205, pp 152-155, 2015 | 5 | 2015 |
| Optimizing the depth and the direction of prospective planning using information values E Sezener, A Dezfouli, M Keramati PLOS Computational Biology 15 (3), e1006827, 2019 | 4 | 2019 |
| Online Learning in Contextual Bandits using Gated Linear Networks E Sezener, M Hutter, D Budden, J Wang, J Veness arXiv preprint arXiv:2002.11611, 2020 | 3 | 2020 |
| Gated Linear Networks J Veness, T Lattimore, A Bhoopchand, D Budden, C Mattern, ... arXiv preprint arXiv:1910.01526, 2019 | 2 | 2019 |
| Gaussian Gated Linear Networks D Budden, A Marblestone, E Sezener, T Lattimore, G Wayne, J Veness arXiv preprint arXiv:2006.05964, 2020 | | 2020 |
| Static and Dynamic Values of Computation in MCTS E Sezener, P Dayan Uncertainty in Artificial Intelligence (UAI), 2020 | | 2020 |
| Computing the Value of Computation for Planning CE Sezener arXiv preprint arXiv:1811.03035, 2018 | | 2018 |
| Algorithms for Obtaining Parsimonious Higher Order Neurons CE Sezener, E Oztop International Conference on Artificial Neural Networks (ICANN), 146-154, 2017 | | 2017 |
| Approximate planning from better bounds on Q CE Sezener, P Dayan Reinforcement Learning and Decision Making (RLDM), 2017 | | 2017 |
David BuddenDeepMindAdresse e-mail validée de csail.mit.edu
