| TRAINING MACHINE LEARNING MODELS BY DETERMINING UPDATE RULES USING NEURAL NETWORKS MMR Denil, T Schaul, M Andrychowicz, G De, FJ Ferdinando, ... US Patent App. 18/180,754, 2023 | | 2023 |
| Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning P Emedom-Nnamdi, AL Friesen, B Shahriari, N de Freitas, MW Hoffman arXiv preprint arXiv:2305.03870, 2023 | | 2023 |
| Training machine learning models by determining update rules using recurrent neural networks MMR Denil, T Schaul, M Andrychowicz, JFG De Freitas, SG Colmenarejo, ... US Patent 11,615,310, 2023 | | 2023 |
| Distributional reinforcement learning for continuous control tasks D Budden, MW Hoffman, G Barth-Maron US Patent 11,481,629, 2022 | 3 | 2022 |
| Launchpad: a programming model for distributed machine learning research F Yang, G Barth-Maron, P Stańczyk, M Hoffman, S Liu, M Kroiss, A Pope, ... arXiv preprint arXiv:2106.04516, 2021 | 16 | 2021 |
| Regularized behavior value estimation C Gulcehre, SG Colmenarejo, Z Wang, J Sygnowski, T Paine, K Zolna, ... arXiv preprint arXiv:2103.09575, 2021 | 24 | 2021 |
| Addressing Extrapolation Error in Deep Offline Reinforcement Learning C Gulcehre, SG Colmenarejo, J Sygnowski, T Paine, K Zolna, Y Chen, ... | 2 | 2020 |
| Acme: A research framework for distributed reinforcement learning MW Hoffman, B Shahriari, J Aslanides, G Barth-Maron, N Momchev, ... arXiv preprint arXiv:2006.00979, 2020 | 214 | 2020 |
| Modular meta-learning with shrinkage Y Chen, AL Friesen, F Behbahani, A Doucet, D Budden, M Hoffman, ... Advances in Neural Information Processing Systems 33, 2858-2869, 2020 | 42 | 2020 |
| Rl unplugged: A suite of benchmarks for offline reinforcement learning C Gulcehre, Z Wang, A Novikov, T Paine, S Gómez, K Zolna, R Agarwal, ... Advances in Neural Information Processing Systems 33, 7248-7259, 2020 | 88 | 2020 |
| Large-scale visual speech recognition B Shillingford, Y Assael, MW Hoffman, T Paine, C Hughes, U Prabhu, ... arXiv preprint arXiv:1807.05162, 2018 | 171 | 2018 |
| Distributed distributional deterministic policy gradients G Barth-Maron, MW Hoffman, D Budden, W Dabney, D Horgan, D Tb, ... arXiv preprint arXiv:1804.08617, 2018 | 549 | 2018 |
| The intentional unintentional agent: Learning to solve many continuous control tasks simultaneously S Cabi, SG Colmenarejo, MW Hoffman, M Denil, Z Wang, N Freitas Conference on Robot Learning, 207-216, 2017 | 37 | 2017 |
| Learned optimizers that scale and generalize O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ... International conference on machine learning, 3751-3760, 2017 | 272 | 2017 |
| Learning to learn without gradient descent by gradient descent Y Chen, MW Hoffman, SG Colmenarejo, M Denil, TP Lillicrap, M Botvinick, ... International Conference on Machine Learning, 748-756, 2017 | 291* | 2017 |
| A general framework for constrained Bayesian optimization using information-based search JM Hernández-Lobato, MA Gelbart, RP Adams, MW Hoffman, ... MIT Press, 2016 | 160 | 2016 |
| Learning to learn by gradient descent by gradient descent M Andrychowicz, M Denil, S Gomez, MW Hoffman, D Pfau, T Schaul, ... Advances in neural information processing systems 29, 2016 | 2079 | 2016 |
| Predictive entropy search for Bayesian optimization with unknown constraints JM Hernández-Lobato, M Gelbart, M Hoffman, R Adams, Z Ghahramani International conference on machine learning, 1699-1707, 2015 | 156 | 2015 |
| Output-space predictive entropy search for flexible global optimization MW Hoffman, Z Ghahramani NIPS workshop on Bayesian Optimization, 1-5, 2015 | 35 | 2015 |
| An entropy search portfolio for Bayesian optimization B Shahriari, Z Wang, MW Hoffman, A Bouchard-Côté, N de Freitas arXiv preprint arXiv:1406.4625, 2014 | 77 | 2014 |
Nando de FreitasCIFAR & DeepMindVerified email at google.com
