| 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 | 2083 | 2016 |
| Predictive entropy search for efficient global optimization of black-box functions JM Hernández-Lobato, MW Hoffman, Z Ghahramani Advances in neural information processing systems 27, 2014 | 711 | 2014 |
| 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 |
| Portfolio Allocation for Bayesian Optimization. MW Hoffman, E Brochu, N de Freitas UAI, 327-336, 2011 | 335 | 2011 |
| 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 |
| Learned optimizers that scale and generalize O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ... International conference on machine learning, 3751-3760, 2017 | 272 | 2017 |
| 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 |
| Large-scale visual speech recognition B Shillingford, Y Assael, MW Hoffman, T Paine, C Hughes, U Prabhu, ... arXiv preprint arXiv:1807.05162, 2018 | 172 | 2018 |
| On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning MW Hoffman, B Shahriari, N de Freitas Proceedings of the Seventeenth International Conference on Artificial …, 2014 | 168* | 2014 |
| A general framework for constrained Bayesian optimization using information-based search JM Hernández-Lobato, MA Gelbart, RP Adams, MW Hoffman, ... MIT Press, 2016 | 161 | 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 | 157 | 2015 |
| A probabilistic model of gaze imitation and shared attention MW Hoffman, DB Grimes, AP Shon, RPN Rao Neural Networks 19 (3), 299-310, 2006 | 103 | 2006 |
| 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 |
| 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 |
| Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization MW Hoffman, A Lazaric, M Ghavamzadeh, R Munos European Workshop on Reinforcement Learning, 102-114, 2011 | 61 | 2011 |
| Finite-sample analysis of Lasso-TD M Ghavamzadeh, A Lazaric, R Munos, MW Hoffman International Conference on Machine Learning, 2011 | 58 | 2011 |
| 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 |
| Bayesian policy learning with trans-dimensional MCMC M Hoffman, A Doucet, N Freitas, A Jasra Advances in neural information processing systems 20, 2007 | 40 | 2007 |
| An expectation maximization algorithm for continuous Markov Decision Processes with arbitrary reward MW Hoffman, N de Freitas, A Doucet, J Peters International Conference on Artificial Intelligence and Statistics, 232-239, 2009 | 39 | 2009 |
| New inference strategies for solving Markov decision processes using reversible jump MCMC M Hoffman, H Kueck, N De Freitas, A Doucet arXiv preprint arXiv:1205.2643, 2012 | 38 | 2012 |
Nando de FreitasCIFAR & DeepMindVerified email at google.com
