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Matthew W. Hoffman

DeepMind

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My research there focused on using inference-based approaches to solve problems in sequential decision making. I also wrote some software for Bayesian optimization that you might find useful.

Papers36 papers

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Modular Meta-Learning with Shrinkage

Yutian Chen,Abram Friesen,Feryal Behbahani,Arnaud Doucet,David Budden,Matthew Hoffman,Nando de Freitas

NIPS 2020, (2020)

EIBibtex

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems

Caglar Gulcehre,Tom Le Paine,Bobak Shahriari,Misha Denil,Matt Hoffman,Hubert Soyer, Richard Tanburn,Steven Kapturowski,Neil Rabinowitz, Duncan Williams, Gabriel Barth-Maron,Ziyu Wang

ICLR, (2020)

Cited by1EIBibtex

Acme: A Research Framework for Distributed Reinforcement Learning

Hoffman Matt,Shahriari Bobak, Aslanides John, Barth-Maron Gabriel, Behbahani Feryal, Norman Tamara, Abdolmaleki Abbas, Cassirer Albin,Yang Fan, Baumli Kate, Henderson Sarah,Novikov Alex

Bibtex

RL Unplugged: Benchmarks for Offline Reinforcement Learning

Caglar Gulcehre,Ziyu Wang,Alexander Novikov,Tom Le Paine, Sergio Gómez Colmenarejo,Konrad Zolna, Rishabh Agarwal,Josh Merel,Daniel Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li

Cited by2Bibtex

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning

Ziyu Wang,Caglar Gulcehre,Alexander Novikov, Thomas Paine, Sergio Gómez,Konrad Zolna, Rishabh Agarwal,Josh Merel,Daniel Mankowitz, Cosmin Paduraru,Gabriel Dulac-Arnold, Jerry Li

NIPS 2020, (2020)

EIBibtex

Modular Meta-Learning with Shrinkage

Chen Yutian,Friesen Abram L.,Behbahani Feryal,Budden David,Hoffman Matthew W.,Doucet Arnaud,de Freitas Nando

Cited by5Bibtex

Large-Scale Visual Speech Recognition

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

arXiv: Computer Vision and Pattern Recognition, (2019): 4135-4139

Cited by43EIBibtex

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL

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

arXiv: Learning, (2018)

Cited by10EIBibtex

Distributed Distributional Deterministic Policy Gradients

Gabriel Barth-Maron,Matthew W. Hoffman,David Budden,Will Dabney, Dan Horgan, Dhruva TB,Alistair Muldal,Nicolas Heess,Timothy P. Lillicrap

ICLR, (2018)

Cited by172EIBibtex

Parameter Prediction Paper: Learning to Learn by gradient descent by gradient descent

Marcin Andrychowicz, Misha Denil, Sergio Gmez Colmenarejo,Matthew W Hoffman,David Pfau, Tom Schaul, Brendan Shillingford

(2018)

Bibtex

Simple, Distributed, and Accelerated Probabilistic Programming

Dustin Tran,Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan,Alexey Radul, Matthew Johnson, Rif A. Saurous

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), (2018): 7598-7609

Cited by31EIBibtex

Learned Optimizers that Scale and Generalize

Olga Wichrowska,Niru Maheswaranathan,Matthew W. Hoffman, Sergio Gomez Colmenarejo,Misha Denil,Nando de Freitas,Jascha Sohl-Dickstein

ICML, (2017)

Cited by104EIBibtex

The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously

Serkan Cabi, Sergio Gomez Colmenarejo,Matthew W. Hoffman,Misha Denil,Ziyu Wang,Nando de Freitas

CoRL, (2017): 207-216

Cited by22EIBibtex

Learning to Learn without Gradient Descent by Gradient Descent

Yutian Chen,Matthew W. Hoffman, Sergio Gomez Colmenarejo,Misha Denil,Timothy P. Lillicrap,Matthew Botvinick,Nando de Freitas

ICML, pp.748-756, (2017)

Cited by133EIBibtex

Learning to learn by gradient descent by gradient descent

Marcin Andrychowicz,Misha Denil, Sergio Gomez Colmenarejo,Matthew W. Hoffman, David Pfau,Tom Schaul,Brendan Shillingford,Nando de Freitas

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016), (2016)

Bibtex

Learning to learn by gradient descent by gradient descent

Marcin Andrychowicz,Misha Denil, Sergio Gomez,Matthew W. Hoffman,David Pfau,Tom Schaul,Nando de Freitas

NIPS, (2016): 3981-3989

Cited by940EIBibtex

A General Framework for Constrained Bayesian Optimization using Information-based Search

Jose Miguel Hernandezlobato,Michael Gelbart,Ryan P Adams,Matthew W Hoffman,Zoubin Ghahramani

Journal of Machine Learning Research, (2015)

Cited by66EIBibtex

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints

José Miguel Hernández-Lobato,Michael A. Gelbart,Matthew W. Hoffman,Ryan P. Adams,Zoubin Ghahramani

International Conference on Machine Learning, (2015)

Cited by77EIBibtex

Output space predictive entropy search for flexible global optimization

Matthew W Hoffman,Zoubin Ghahramani

NIPS workshop on Bayesian Optimization, pp.1-5, (2015)

Cited by12Bibtex

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions

José Miguel Hernández-Lobato,Matthew W. Hoffman,Zoubin Ghahramani

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 27 (NIPS 2014), (2014): 918-926

Cited by361EIBibtex

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