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 (ICRA, 2005)

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)

Bayesian Policy Learning with Trans-Dimensional MCMC. - Matt Hoffman 001; Arnaud Doucet; Nando de Freitas; Ajay Jasra (NIPS, 2007)

Inference and Learning for Active Sensing, Experimental Design and Control. - Hendrik Kück; Matt Hoffman 001; Arnaud Doucet; Nando de Freitas (IbPRIA, 2009)

An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward. - Matthew Hoffman 002; Nando de Freitas; Arnaud Doucet; Jan Peters 001 (AISTATS, 2009)

Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization. - Matthew W. Hoffman; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos (EWRL, 2011)

Finite-Sample Analysis of Lasso-TD. - Mohammad Ghavamzadeh; Alessandro Lazaric; Rémi Munos; Matthew W. Hoffman (ICML, 2011)

Portfolio Allocation for Bayesian Optimization. - Matthew Hoffman 002; Eric Brochu; Nando de Freitas (UAI, 2011)

Best arm identification via Bayesian gap-based exploration - Matthew W. Hoffman; Bobak Shahriari; Nando de Freitas (CoRR, 2013)

On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. - Matthew W. Hoffman; Bobak Shahriari; Nando de Freitas (AISTATS, 2014)

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. - José Miguel Hernández-Lobato; Matthew W. Hoffman; Zoubin Ghahramani (NIPS, 2014)

An Entropy Search Portfolio for Bayesian Optimization. - Bobak Shahriari; Ziyu Wang 001; Matthew W. Hoffman; Alexandre Bouchard-Côté; Nando de Freitas (CoRR, 2014)

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints. - José Miguel Hernández-Lobato; Michael A. Gelbart; Matthew W. Hoffman; Ryan P. Adams; Zoubin Ghahramani (ICML, 2015)

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 (NIPS, 2016)

Learning to Learn for Global Optimization of Black Box Functions. - Yutian Chen 001; Matthew W. Hoffman; Sergio Gomez Colmenarejo; Misha Denil; Timothy P. Lillicrap; Nando de Freitas (CoRR, 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)

The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously. - Serkan Cabi; Sergio Gomez Colmenarejo; Matthew W. Hoffman; Misha Denil; Ziyu Wang 001; Nando de Freitas (CoRL, 2017)

Learning to Learn without Gradient Descent by Gradient Descent. - Yutian Chen 001; Matthew W. Hoffman; Sergio Gomez Colmenarejo; Misha Denil; Timothy P. Lillicrap; Matthew M. Botvinick; Nando de Freitas (ICML, 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 (ICML, 2017)

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 (Poster), 2018)

Synthesizing Diverse, High-Quality Audio Textures. - Joseph M. Antognini; Matt Hoffman 001; Ron J. Weiss (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; et al. (CoRR, 2018)

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL. - Tom Le Paine; Sergio Gomez Colmenarejo; Ziyu Wang 001; Scott E. Reed; Yusuf Aytar; Tobias Pfaff; Matthew W. Hoffman; Gabriel Barth-Maron; Serkan Cabi; David Budden; Nando de Freitas (CoRR, 2018)

Audio Texture Synthesis with Random Neural Networks: Improving Diversity and Quality. - Joseph M. Antognini; Matt Hoffman 001; Ron J. Weiss (ICASSP, 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; et al. (INTERSPEECH, 2019)

Improving the Gating Mechanism of Recurrent Neural Networks. - Albert Gu; Çaglar Gülçehre; Tom Le Paine; Matthew W. Hoffman; Razvan Pascanu (CoRR, 2019)

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems. - Çaglar Gülçehre; Tom Le Paine; Bobak Shahriari; Misha Denil; Matt Hoffman 001; Hubert Soyer; Richard Tanburn; Steven Kapturowski; Neil C. Rabinowitz; Duncan Williams; Gabriel Barth-Maron; Ziyu Wang 001; et al. (ICLR, 2020)

Improving the Gating Mechanism of Recurrent Neural Networks. - Albert Gu; Çaglar Gülçehre; Thomas Paine; Matt Hoffman 001; Razvan Pascanu (ICML, 2020)

Modular Meta-Learning with Shrinkage. - Yutian Chen 001; Abram L. Friesen; Feryal M. P. Behbahani; Arnaud Doucet; David Budden; Matthew Hoffman 002; Nando de Freitas (NeurIPS, 2020)

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Thomas Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (NeurIPS, 2020)

Acme: A Research Framework for Distributed Reinforcement Learning. - Matt Hoffman 001; 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 001; et al. (CoRR, 2020)

RL Unplugged: Benchmarks for Offline Reinforcement Learning. - Çaglar Gülçehre; Ziyu Wang 001; Alexander Novikov 001; Tom Le Paine; Sergio Gómez Colmenarejo; Konrad Zolna; Rishabh Agarwal; Josh Merel; Daniel J. Mankowitz; Cosmin Paduraru; Gabriel Dulac-Arnold; Jerry Li; et al. (CoRR, 2020)

Regularized Behavior Value Estimation. - Çaglar Gülçehre; Sergio Gómez Colmenarejo; Ziyu Wang 001; Jakub Sygnowski; Thomas Paine; Konrad Zolna; Yutian Chen 001; Matthew W. Hoffman; Razvan Pascanu; Nando de Freitas (CoRR, 2021)

Launchpad: A Programming Model for Distributed Machine Learning Research. - Fan Yang; Gabriel Barth-Maron; Piotr Stanczyk; Matthew W. Hoffman; Siqi Liu 002; Manuel Kroiss; Aedan Pope; Alban Rrustemi (CoRR, 2021)

Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach. - Bobak Shahriari; Abbas Abdolmaleki; Arunkumar Byravan; Abe Friesen; Siqi Liu 002; Jost Tobias Springenberg; Nicolas Heess; Matt Hoffman 001; Martin A. Riedmiller (CoRR, 2022)

An empirical study of implicit regularization in deep offline RL. - Çaglar Gülçehre; Srivatsan Srinivasan; Jakub Sygnowski; Georg Ostrovski; Mehrdad Farajtabar; Matthew Hoffman 002; Razvan Pascanu; Arnaud Doucet (Trans. Mach. Learn. Res., 2022)

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)

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; et al. (CoRR, 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; et al. (CoRR, 2024)

BOND: Aligning LLMs with Best-of-N Distillation. - Pier Giuseppe Sessa; Robert Dadashi-Tazehozi; Léonard Hussenot; Johan Ferret; Nino Vieillard; Alexandre Ramé; Bobak Shahriari; Sarah Perrin; Abram L. Friesen; Geoffrey Cideron; Sertan Girgin; Piotr Stanczyk; et al. (ICLR, 2025)

Gemma 3 Technical Report. - Aishwarya Kamath; Johan Ferret; Shreya Pathak; Nino Vieillard; Ramona Merhej; Sarah Perrin; Tatiana Matejovicova; Alexandre Ramé; Morgane Rivière; Louis Rouillard; Thomas Mesnard; Geoffrey Cideron; et al. (CoRR, 2025)