Matthew W. Hoffman

Publication Activity (10 Years)

Years Active: 2005-2023
Publications (10 Years): 20

Top Topics

Visual Speech Recognition

Global Optimization

Reinforcement Learning

Top Venues

Publications

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 (2023)
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 (2021)
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 (2021)
Albert Gu, Çaglar Gülçehre, Tom Le Paine, Matthew W. Hoffman, Razvan Pascanu
Improving the Gating Mechanism of Recurrent Neural Networks. CoRR (2019)
Yutian Chen, Abram L. Friesen, Feryal Behbahani, David Budden, Matthew W. Hoffman, Arnaud Doucet, Nando de Freitas
Modular Meta-Learning with Shrinkage. CoRR (2019)
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)
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 (2018)
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 (2018)
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)
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 (2018)
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)
Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matthew Botvinick, Nando de Freitas
Learning to Learn without Gradient Descent by Gradient Descent. ICML (2017)
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)
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 (2017)
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 (2017)
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 (2016)
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 (2016)
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 (2016)
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)
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)
Bobak Shahriari, Ziyu Wang, Matthew W. Hoffman, Alexandre Bouchard-Côté, Nando de Freitas
An Entropy Search Portfolio for Bayesian Optimization. CoRR (2014)
José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. NIPS (2014)
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)
José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. CoRR (2014)
Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas
Best arm identification via Bayesian gap-based exploration CoRR (2013)
Matthew W. Hoffman, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos
Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization. EWRL (2011)
Mohammad Ghavamzadeh, Alessandro Lazaric, Rémi Munos, Matthew W. Hoffman
Finite-Sample Analysis of Lasso-TD. ICML (2011)
Eric Brochu, Matthew W. Hoffman, Nando de Freitas
Portfolio Allocation for Bayesian Optimization CoRR (2010)
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) (2006)
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)