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ORCIDMatt Hoffman 0001
Person information
- affiliation: Google DeepMind, London, UK
- affiliation: University of British Columbia, Department of Computer Science, Vancouver, Canada
- affiliation: University of Cambridge, Machine Learning Group, UK
Other persons with the same name
- Matt Hoffman 0002 (aka: Matt S. Hoffman) — Utah Health Information Network, Murray, UT, USA
- Matthew Hoffman 0001 (aka: Matthew D. Hoffman, Matthew Douglas Hoffman) — Adobe Research (and 2 more)
- Matthew Hoffman 0003 — National Museum of American History, Smithsonian Institution, Washington, DC, USA
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Journal Articles
- 2022
[j3]Çaglar Gülçehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matthew Hoffman, Razvan Pascanu, Arnaud Doucet:
An empirical study of implicit regularization in deep offline RL. Trans. Mach. Learn. Res. 2022 (2022)- 2016
- [j2]José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani:
A General Framework for Constrained Bayesian Optimization using Information-based Search. J. Mach. Learn. Res. 17: 160:1-160:53 (2016) - 2006
- [j1]Matthew W. Hoffman, David B. Grimes, Aaron P. Shon, Rajesh P. N. Rao
:
A probabilistic model of gaze imitation and shared attention. Neural Networks 19(3): 299-310 (2006)
Conference and Workshop Papers
- 2020
- [c21]Çaglar Gülçehre, Tom Le Paine, Bobak Shahriari, Misha Denil, Matt Hoffman, Hubert Soyer, Richard Tanburn, Steven Kapturowski, Neil C. Rabinowitz, Duncan Williams, Gabriel Barth-Maron, Ziyu Wang, Nando de Freitas, Worlds Team:
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems. ICLR 2020 - [c20]Albert Gu, Çaglar Gülçehre, Thomas Paine, Matt Hoffman, Razvan Pascanu:
Improving the Gating Mechanism of Recurrent Neural Networks. ICML 2020: 3800-3809 - [c19]Yutian Chen, Abram L. Friesen, Feryal M. P. Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas:
Modular Meta-Learning with Shrinkage. NeurIPS 2020 - [c18]Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas:
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. NeurIPS 2020 - 2019
- [c17]Joseph M. Antognini, Matt Hoffman, Ron J. Weiss:
Audio Texture Synthesis with Random Neural Networks: Improving Diversity and Quality. ICASSP 2019: 3587-3591 - [c16]Brendan Shillingford, Yannis M. Assael, Matthew W. Hoffman, Thomas Paine, Cían Hughes, Utsav Prabhu, Hank Liao, Hasim Sak, Kanishka Rao, Lorrayne Bennett, Marie Mulville, Misha Denil, Ben Coppin, Ben Laurie, Andrew W. Senior, Nando de Freitas:
Large-Scale Visual Speech Recognition. INTERSPEECH 2019: 4135-4139 - 2018
- [c15]Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap:
Distributed Distributional Deterministic Policy Gradients. ICLR (Poster) 2018 - 2017
- [c14]Serkan Cabi, Sergio Gomez Colmenarejo, Matthew W. Hoffman, Misha Denil, Ziyu Wang, Nando de Freitas:
The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously. CoRL 2017: 207-216 - [c13]Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew M. Botvinick, Nando de Freitas:
Learning to Learn without Gradient Descent by Gradient Descent. ICML 2017: 748-756 - [c12]Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein:
Learned Optimizers that Scale and Generalize. ICML 2017: 3751-3760 - 2016
- [c11]Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas:
Learning to learn by gradient descent by gradient descent. NIPS 2016: 3981-3989 - 2015
- [c10]José Miguel Hernández-Lobato, Michael A. Gelbart, Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani:
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints. ICML 2015: 1699-1707 - 2014
- [c9]Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas:
On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. AISTATS 2014: 365-374 - [c8]José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani:
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. NIPS 2014: 918-926 - 2011
- [c7]Matthew W. Hoffman, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos:
Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization. EWRL 2011: 102-114 - [c6]Mohammad Ghavamzadeh, Alessandro Lazaric, Rémi Munos, Matthew W. Hoffman:
Finite-Sample Analysis of Lasso-TD. ICML 2011: 1177-1184 - [c5]Matthew Hoffman, Eric Brochu, Nando de Freitas:
Portfolio Allocation for Bayesian Optimization. UAI 2011: 327-336 - 2009
- [c4]Hendrik Kück, Matt Hoffman, Arnaud Doucet, Nando de Freitas:
Inference and Learning for Active Sensing, Experimental Design and Control. IbPRIA 2009: 1-10 - [c3]Matthew Hoffman, Nando de Freitas, Arnaud Doucet, Jan Peters:
An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward. AISTATS 2009: 232-239 - 2007
- [c2]Matt Hoffman, Arnaud Doucet, Nando de Freitas, Ajay Jasra:
Bayesian Policy Learning with Trans-Dimensional MCMC. NIPS 2007: 665-672 - 2005
- [c1]Aaron P. Shon, David B. Grimes, Chris L. Baker, Matthew W. Hoffman, Shengli Zhou, Rajesh P. N. Rao:
Probabilistic Gaze Imitation and Saliency Learning in a Robotic Head. ICRA 2005: 2865-2870
Informal and Other Publications
- 2023
- [i22]Patrick Emedom-Nnamdi, Abram L. Friesen, Bobak Shahriari, Nando de Freitas, Matthew W. Hoffman:
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning. CoRR abs/2305.03870 (2023) - 2022
- [i21]Bobak Shahriari, Abbas Abdolmaleki, Arunkumar Byravan, Abe Friesen, Siqi Liu, Jost Tobias Springenberg, Nicolas Heess, Matt Hoffman, Martin A. Riedmiller:
Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach. CoRR abs/2204.10256 (2022) - [i20]Çaglar Gülçehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matt Hoffman, Razvan Pascanu, Arnaud Doucet:
An Empirical Study of Implicit Regularization in Deep Offline RL. CoRR abs/2207.02099 (2022) - 2021
- [i19]Çaglar Gülçehre, Sergio Gómez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew W. Hoffman, Razvan Pascanu, Nando de Freitas:
Regularized Behavior Value Estimation. CoRR abs/2103.09575 (2021) - [i18]Fan Yang, Gabriel Barth-Maron, Piotr Stanczyk, Matthew W. Hoffman, Siqi Liu, Manuel Kroiss, Aedan Pope, Alban Rrustemi:
Launchpad: A Programming Model for Distributed Machine Learning Research. CoRR abs/2106.04516 (2021) - 2020
- [i17]Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal M. P. Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alexander Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Çaglar Gülçehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas:
Acme: A Research Framework for Distributed Reinforcement Learning. CoRR abs/2006.00979 (2020) - [i16]Çaglar Gülçehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas:
RL Unplugged: Benchmarks for Offline Reinforcement Learning. CoRR abs/2006.13888 (2020) - 2019
- [i15]Tom Le Paine, Çaglar Gülçehre, Bobak Shahriari, Misha Denil, Matt Hoffman, Hubert Soyer, Richard Tanburn, Steven Kapturowski, Neil C. Rabinowitz, Duncan Williams, Gabriel Barth-Maron, Ziyu Wang, Nando de Freitas, Worlds Team:
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems. CoRR abs/1909.01387 (2019) - [i14]Yutian Chen, Abram L. Friesen, Feryal M. P. Behbahani, David Budden, Matthew W. Hoffman, Arnaud Doucet, Nando de Freitas:
Modular Meta-Learning with Shrinkage. CoRR abs/1909.05557 (2019) - [i13]Albert Gu, Çaglar Gülçehre, Tom Le Paine, Matthew W. Hoffman, Razvan Pascanu:
Improving the Gating Mechanism of Recurrent Neural Networks. CoRR abs/1910.09890 (2019) - 2018
- [i12]Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy P. Lillicrap:
Distributed Distributional Deterministic Policy Gradients. CoRR abs/1804.08617 (2018) - [i11]Joseph M. Antognini, Matt Hoffman, Ron J. Weiss:
Synthesizing Diverse, High-Quality Audio Textures. CoRR abs/1806.08002 (2018) - [i10]Brendan Shillingford, Yannis M. Assael, Matthew W. Hoffman, Thomas Paine, Cían Hughes, Utsav Prabhu, Hank Liao, Hasim Sak, Kanishka Rao, Lorrayne Bennett, Marie Mulville, Ben Coppin, Ben Laurie, Andrew W. Senior, Nando de Freitas:
Large-Scale Visual Speech Recognition. CoRR abs/1807.05162 (2018) - [i9]Tom Le Paine, Sergio Gomez Colmenarejo, Ziyu Wang, Scott E. Reed, Yusuf Aytar, Tobias Pfaff, Matthew W. Hoffman, Gabriel Barth-Maron, Serkan Cabi, David Budden, Nando de Freitas:
One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL. CoRR abs/1810.05017 (2018) - 2017
- [i8]Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando de Freitas, Jascha Sohl-Dickstein:
Learned Optimizers that Scale and Generalize. CoRR abs/1703.04813 (2017) - [i7]Serkan Cabi, Sergio Gomez Colmenarejo, Matthew W. Hoffman, Misha Denil, Ziyu Wang, Nando de Freitas:
The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously. CoRR abs/1707.03300 (2017) - 2016
- [i6]Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas:
Learning to learn by gradient descent by gradient descent. CoRR abs/1606.04474 (2016) - [i5]Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Nando de Freitas:
Learning to Learn for Global Optimization of Black Box Functions. CoRR abs/1611.03824 (2016) - 2014
- [i4]José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani:
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. CoRR abs/1406.2541 (2014) - [i3]Bobak Shahriari, Ziyu Wang, Matthew W. Hoffman, Alexandre Bouchard-Côté, Nando de Freitas:
An Entropy Search Portfolio for Bayesian Optimization. CoRR abs/1406.4625 (2014) - 2013
- [i2]Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas:
Best arm identification via Bayesian gap-based exploration. CoRR abs/1303.6746 (2013) - 2010
- [i1]Eric Brochu, Matthew W. Hoffman, Nando de Freitas:
Portfolio Allocation for Bayesian Optimization. CoRR abs/1009.5419 (2010)
Coauthor Index
aka: Sergio Gómez Colmenarejo
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