Matt Hoffman

According to our database, Matt Hoffman authored at least 39 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Gemma 2: Improving Open Language Models at a Practical Size.

Morgane Rivière, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, Johan Ferret, Peter Liu, Pouya Tafti, Abe Friesen, Michelle Casbon, Sabela Ramos, Ravin Kumar, Charline Le Lan, Sammy Jerome, Anton Tsitsulin, Nino Vieillard, Piotr Stanczyk, Sertan Girgin, Nikola Momchev, Matt Hoffman, Shantanu Thakoor, Jean-Bastien Grill, Behnam Neyshabur, Olivier Bachem, Alanna Walton, Aliaksei Severyn, Alicia Parrish, Aliya Ahmad, Allen Hutchison, Alvin Abdagic, Amanda Carl, Amy Shen, Andy Brock, Andy Coenen, Anthony Laforge, Antonia Paterson, Ben Bastian, Bilal Piot, Bo Wu, Brandon Royal, Charlie Chen, Chintu Kumar, Chris Perry, Chris Welty, Christopher A. Choquette-Choo, Danila Sinopalnikov, David Weinberger, Dimple Vijaykumar, Dominika Rogozinska, Dustin Herbison, Elisa Bandy, Emma Wang, Eric Noland, Erica Moreira, Evan Senter, Evgenii Eltyshev, Francesco Visin, Gabriel Rasskin, Gary Wei, Glenn Cameron, Gus Martins, Hadi Hashemi, Hanna Klimczak-Plucinska, Harleen Batra, Harsh Dhand, Ivan Nardini, Jacinda Mein, Jack Zhou, James Svensson, Jeff Stanway, Jetha Chan, Jin Peng Zhou, Joana Carrasqueira, Joana Iljazi, Jocelyn Becker, Joe Fernandez, Joost van Amersfoort, Josh Gordon, Josh Lipschultz, Josh Newlan, Ju-yeong Ji, Kareem Mohamed, Kartikeya Badola, Kat Black, Katie Millican, Keelin McDonell, Kelvin Nguyen, Kiranbir Sodhia, Kish Greene, Lars Lowe Sjösund, Lauren Usui, Laurent Sifre, Lena Heuermann, Leticia Lago, Lilly McNealus

CoRR, 2024

BOND: Aligning LLMs with Best-of-N Distillation.

Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Nino Vieillard, Alexandre Ramé, Bobak Shahriari, Sarah Perrin, Abe Friesen, Geoffrey Cideron, Sertan Girgin, Piotr Stanczyk, Andrea Michi, Danila Sinopalnikov, Sabela Ramos, Amélie Héliou, Aliaksei Severyn, Matt Hoffman, Nikola Momchev, Olivier Bachem

CoRR, 2024

2023

Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning.

Patrick Emedom-Nnamdi, Abram L. Friesen, Bobak Shahriari, Nando de Freitas, Matthew W. Hoffman

CoRR, 2023

2022

An empirical study of implicit regularization in deep offline RL.

Çaglar Gülçehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matthew Hoffman, Razvan Pascanu, Arnaud Doucet

Trans. Mach. Learn. Res., 2022

Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach.

Bobak Shahriari, Abbas Abdolmaleki, Arunkumar Byravan, Abe Friesen, Siqi Liu, Jost Tobias Springenberg, Nicolas Heess, Matt Hoffman, Martin A. Riedmiller

CoRR, 2022

2021

Launchpad: A Programming Model for Distributed Machine Learning Research.

Fan Yang, Gabriel Barth-Maron, Piotr Stanczyk, Matthew W. Hoffman, Siqi Liu, Manuel Kroiss, Aedan Pope, Alban Rrustemi

CoRR, 2021

Regularized Behavior Value Estimation.

Çaglar Gülçehre, Sergio Gómez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew W. Hoffman, Razvan Pascanu, Nando de Freitas

CoRR, 2021

2020

RL Unplugged: Benchmarks for Offline Reinforcement Learning.

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

CoRR, 2020

Acme: A Research Framework for Distributed Reinforcement Learning.

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

CoRR, 2020

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning.

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Modular Meta-Learning with Shrinkage.

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Improving the Gating Mechanism of Recurrent Neural Networks.

Albert Gu, Çaglar Gülçehre, Thomas Paine, Matt Hoffman, Razvan Pascanu

Proceedings of the 37th International Conference on Machine Learning, 2020

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems.

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

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Improving the Gating Mechanism of Recurrent Neural Networks.

Albert Gu, Çaglar Gülçehre, Tom Le Paine, Matthew W. Hoffman, Razvan Pascanu

CoRR, 2019

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, Misha Denil, Ben Coppin, Ben Laurie, Andrew W. Senior, Nando de Freitas

Proceedings of the 20th Annual Conference of the International Speech Communication Association, 2019

Audio Texture Synthesis with Random Neural Networks: Improving Diversity and Quality.

Joseph M. Antognini, Matt Hoffman, Ron J. Weiss

Proceedings of the IEEE International Conference on Acoustics, 2019

2018

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

CoRR, 2018

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, Ben Laurie, Andrew W. Senior, Nando de Freitas

CoRR, 2018

Synthesizing Diverse, High-Quality Audio Textures.

Joseph M. Antognini, Matt Hoffman, Ron J. Weiss

CoRR, 2018

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

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Learned Optimizers that Scale and Generalize.

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

Proceedings of the 34th International Conference on Machine Learning, 2017

Learning to Learn without Gradient Descent by Gradient Descent.

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

Proceedings of the 34th International Conference on Machine Learning, 2017

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

Proceedings of the 1st Annual Conference on Robot Learning, CoRL 2017, Mountain View, 2017

2016

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

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

J. Mach. Learn. Res., 2016

Learning to Learn for Global Optimization of Black Box Functions.

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

CoRR, 2016

Learning to learn by gradient descent by gradient descent.

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

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints.

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

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

An Entropy Search Portfolio for Bayesian Optimization.

Bobak Shahriari, Ziyu Wang, Matthew W. Hoffman, Alexandre Bouchard-Côté, Nando de Freitas

CoRR, 2014

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

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

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning.

Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Best arm identification via Bayesian gap-based exploration

Matthew W. Hoffman, Bobak Shahriari, Nando de Freitas

CoRR, 2013

2011

Portfolio Allocation for Bayesian Optimization.

Matthew Hoffman, Eric Brochu, Nando de Freitas

Proceedings of the UAI 2011, 2011

Finite-Sample Analysis of Lasso-TD.

Mohammad Ghavamzadeh, Alessandro Lazaric, Rémi Munos, Matthew W. Hoffman

Proceedings of the 28th International Conference on Machine Learning, 2011

Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization.

Matthew W. Hoffman, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos

Proceedings of the Recent Advances in Reinforcement Learning - 9th European Workshop, 2011

2009

An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward.

Matthew Hoffman, Nando de Freitas, Arnaud Doucet, Jan Peters

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Inference and Learning for Active Sensing, Experimental Design and Control.

Hendrik Kück, Matt Hoffman, Arnaud Doucet, Nando de Freitas

Proceedings of the Pattern Recognition and Image Analysis, 4th Iberian Conference, 2009

2007

Bayesian Policy Learning with Trans-Dimensional MCMC.

Matt Hoffman, Arnaud Doucet, Nando de Freitas, Ajay Jasra

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006

A probabilistic model of gaze imitation and shared attention.

Matthew W. Hoffman, David B. Grimes, Aaron P. Shon, Rajesh P. N. Rao

Neural Networks, 2006

2005

Probabilistic Gaze Imitation and Saliency Learning in a Robotic Head.

Aaron P. Shon, David B. Grimes, Chris L. Baker, Matthew W. Hoffman, Shengli Zhou, Rajesh P. N. Rao

Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005