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Matthew W. Hoffman
Matthew W. Hoffman
DeepMind
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Title
Cited by
Cited by
Year
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
17022016
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
5912014
Distributed distributional deterministic policy gradients
G Barth-Maron, MW Hoffman, D Budden, W Dabney, D Horgan, D Tb, ...
arXiv preprint arXiv:1804.08617, 2018
4252018
Portfolio Allocation for Bayesian Optimization.
MW Hoffman, E Brochu, N de Freitas
UAI, 327-336, 2011
2692011
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
242*2017
Learned optimizers that scale and generalize
O Wichrowska, N Maheswaranathan, MW Hoffman, SG Colmenarejo, ...
International Conference on Machine Learning, 3751-3760, 2017
2142017
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
145*2014
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
1332015
Acme: A research framework for distributed reinforcement learning
M Hoffman, B Shahriari, J Aslanides, G Barth-Maron, F Behbahani, ...
arXiv preprint arXiv:2006.00979, 2020
1272020
Large-scale visual speech recognition
B Shillingford, Y Assael, MW Hoffman, T Paine, C Hughes, U Prabhu, ...
arXiv preprint arXiv:1807.05162, 2018
1262018
A general framework for constrained Bayesian optimization using information-based search
JM Hernández-Lobato, MA Gelbart, RP Adams, MW Hoffman, ...
MIT Press, 2016
1262016
A probabilistic model of gaze imitation and shared attention
MW Hoffman, DB Grimes, AP Shon, RPN Rao
Neural Networks 19 (3), 299-310, 2006
1012006
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
632014
Simple, distributed, and accelerated probabilistic programming
D Tran, MW Hoffman, D Moore, C Suter, S Vasudevan, A Radul
Advances in Neural Information Processing Systems 31, 2018
622018
Finite-sample analysis of Lasso-TD
M Ghavamzadeh, A Lazaric, R Munos, MW Hoffman
International Conference on Machine Learning, 2011
552011
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
492011
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
462020
Bayesian policy learning with trans-dimensional MCMC
M Hoffman, A Doucet, N Freitas, A Jasra
Advances in neural information processing systems 20, 2007
412007
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
392005
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
382009
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