| Uncertainty in neural networks: Approximately Bayesian ensembling T Pearce, F Leibfried, A Brintrup, M Zaki, A Neely International Conference on Artificial Intelligence and Statistics (AISTATS), 2020 | 251 | 2020 |
| Bounded rationality, abstraction, and hierarchical decision-making: An information-theoretic optimality principle T Genewein, F Leibfried, J Grau-Moya, DA Braun Frontiers in Robotics and AI, 2015 | 121 | 2015 |
| Soft Q-learning with mutual-information regularization J Grau-Moya, F Leibfried, P Vrancx International Conference on Learning Representations (ICLR), 2019 | 63 | 2019 |
| A deep learning approach for joint video frame and reward prediction in Atari games F Leibfried, N Kushman, K Hofmann ICML Workshop on Principled Approaches to Deep Learning, 2017 | 62 | 2017 |
| Balancing two-player stochastic games with soft Q-learning J Grau-Moya, F Leibfried, H Bou-Ammar International Joint Conference on Artificial Intelligence (IJCAI), 2018 | 58 | 2018 |
| A tutorial on sparse Gaussian processes and variational inference F Leibfried, V Dutordoir, ST John, N Durrande arXiv preprint arXiv:2012.13962, 2020 | 56 | 2020 |
| Signaling equilibria in sensorimotor interactions F Leibfried, J Grau-Moya, DA Braun Cognition, 2015 | 48 | 2015 |
| A unified Bellman optimality principle combining reward maximization and empowerment F Leibfried, S Pascual-Diaz, J Grau-Moya Conference on Neural Information Processing Systems (NeurIPS), 2019 | 39 | 2019 |
| Planning with information-processing constraints and model uncertainty in Markov decision processes J Grau-Moya, F Leibfried, T Genewein, DA Braun European Conference on Machine Learning (ECML PKDD), 2016 | 38 | 2016 |
| An information-theoretic optimality principle for deep reinforcement learning F Leibfried, J Grau-Moya, H Bou-Ammar NeurIPS Workshop on Deep Reinforcement Learning, 2018 | 32 | 2018 |
| GPflux: A library for deep Gaussian processes V Dutordoir, H Salimbeni, E Hambro, J McLeod, F Leibfried, A Artemev, ... arXiv preprint arXiv:2104.05674, 2021 | 30 | 2021 |
| System architecture for an artificial intelligence platform A Tukiainen, D Kim, T Nicholson, M Tomczak, JEMDC Flores, N Ferguson, ... US Patent App. 16/753,580, 2020 | 30* | 2020 |
| Mutual-information regularization in Markov decision processes and actor-critic learning F Leibfried, J Grau-Moya Conference on Robot Learning (CoRL), 2019 | 25 | 2019 |
| Model-based regularization for deep reinforcement learning with transcoder networks F Leibfried, P Vrancx NeurIPS Workshop on Deep Reinforcement Learning, 2018 | 25* | 2018 |
| A reward-maximizing spiking neuron as a bounded rational decision maker F Leibfried, DA Braun Neural Computation, 2015 | 22 | 2015 |
| An information-theoretic on-line update principle for perception-action coupling Z Peng, T Genewein, F Leibfried, DA Braun IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017 | 18 | 2017 |
| Bounded rational decision-making in feedforward neural networks F Leibfried, DA Braun Conference on Uncertainty in Artificial Intelligence (UAI), 2016 | 16 | 2016 |
| Bellman: A toolbox for model-based reinforcement learning in TensorFlow J McLeod, H Stojic, V Adam, D Kim, J Grau-Moya, P Vrancx, F Leibfried arXiv preprint arXiv:2103.14407, 2021 | 2 | 2021 |
| Tuneable artificial intelligence J Grau-Moya, F Leibfried, H Bou-Ammar US Patent App. 16/759,241, 2020 | 2* | 2020 |
| Variational inference for model-free and model-based reinforcement learning F Leibfried arXiv preprint arXiv:2209.01693, 2022 | | 2022 |
Jordi Grau-MoyaResearch Scientist at Google DeepMindVerified email at deepmind.com