| 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, 3981-3989, 2016 | 1043 | 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, 918-926, 2014 | 411 | 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 | 207 | 2018 |
| Portfolio Allocation for Bayesian Optimization. MW Hoffman, E Brochu, N de Freitas UAI, 327-336, 2011 | 181 | 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 | 142* | 2017 |
| Learned optimizers that scale and generalize O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ... arXiv preprint arXiv:1703.04813, 2017 | 109 | 2017 |
| 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 | 99* | 2014 |
| A probabilistic model of gaze imitation and shared attention MW Hoffman, DB Grimes, AP Shon, RPN Rao Neural Networks 19 (3), 299-310, 2006 | 96 | 2006 |
| 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 | 85 | 2015 |
| A general framework for constrained Bayesian optimization using information-based search JM Hernández-Lobato, MA Gelbart, RP Adams, MW Hoffman, ... The Journal of Machine Learning Research 17 (1), 5549-5601, 2016 | 74 | 2016 |
| Finite-sample analysis of Lasso-TD M Ghavamzadeh, A Lazaric, R Munos, MW Hoffman International Conference on Machine Learning, 2011 | 55 | 2011 |
| Large-scale visual speech recognition B Shillingford, Y Assael, MW Hoffman, T Paine, C Hughes, U Prabhu, ... arXiv preprint arXiv:1807.05162, 2018 | 53 | 2018 |
| Trans-dimensional MCMC for Bayesian Policy Learning MW Hoffman, A Doucet, N de Freitas, A Jasra Advances in neural information processing systems, 665-672, 2007 | 47* | 2007 |
| 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 | 45 | 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 | 42 | 2011 |
| 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 | 37 | 2012 |
| 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 | 35 | 2009 |
| Probabilistic gaze imitation and saliency learning in a robotic head AP Shon, DB Grimes, CL Baker, MW Hoffman, S Zhou, RPN Rao Proceedings of the 2005 IEEE International Conference on Robotics and …, 2005 | 33 | 2005 |
| Modular mechanisms for Bayesian optimization MW Hoffman, B Shahriari NIPS workshop on Bayesian optimization, 1-5, 2014 | 27 | 2014 |
| The intentional unintentional agent: Learning to solve many continuous control tasks simultaneously S Cabi, SG Colmenarejo, MW Hoffman, M Denil, Z Wang, N De Freitas arXiv preprint arXiv:1707.03300, 2017 | 26 | 2017 |
Nando de FreitasCIFAR & DeepMindVerified email at google.com
