Matt Hoffman

Affiliations:
  • Google DeepMind, London, UK
  • University of British Columbia, Department of Computer Science, Vancouver, Canada
  • University of Cambridge, Machine Learning Group, UK


According to our database1, Matt Hoffman authored at least 37 papers between 2005 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
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PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2023
Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning.
CoRR, 2023

2022
An empirical study of implicit regularization in deep offline RL.
Trans. Mach. Learn. Res., 2022

Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach.
CoRR, 2022

2021
Launchpad: A Programming Model for Distributed Machine Learning Research.
CoRR, 2021

Regularized Behavior Value Estimation.
CoRR, 2021

2020
RL Unplugged: Benchmarks for Offline Reinforcement Learning.
CoRR, 2020

Acme: A Research Framework for Distributed Reinforcement Learning.
CoRR, 2020

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Modular Meta-Learning with Shrinkage.
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.
Proceedings of the 37th International Conference on Machine Learning, 2020

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Improving the Gating Mechanism of Recurrent Neural Networks.
CoRR, 2019


Audio Texture Synthesis with Random Neural Networks: Improving Diversity and Quality.
Proceedings of the IEEE International Conference on Acoustics, 2019

2018
One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL.
CoRR, 2018

Large-Scale Visual Speech Recognition.
CoRR, 2018

Synthesizing Diverse, High-Quality Audio Textures.
CoRR, 2018

Distributed Distributional Deterministic Policy Gradients.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Learned Optimizers that Scale and Generalize.
Proceedings of the 34th International Conference on Machine Learning, 2017

Learning to Learn without Gradient Descent by Gradient Descent.
Proceedings of the 34th International Conference on Machine Learning, 2017

The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously.
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.
J. Mach. Learn. Res., 2016

Learning to Learn for Global Optimization of Black Box Functions.
CoRR, 2016

Learning to learn by gradient descent by gradient descent.
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.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
An Entropy Search Portfolio for Bayesian Optimization.
CoRR, 2014

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions.
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.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Best arm identification via Bayesian gap-based exploration
CoRR, 2013

2011
Portfolio Allocation for Bayesian Optimization.
Proceedings of the UAI 2011, 2011

Finite-Sample Analysis of Lasso-TD.
Proceedings of the 28th International Conference on Machine Learning, 2011

Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization.
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.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Inference and Learning for Active Sensing, Experimental Design and Control.
Proceedings of the Pattern Recognition and Image Analysis, 4th Iberian Conference, 2009

2007
Bayesian Policy Learning with Trans-Dimensional MCMC.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006
A probabilistic model of gaze imitation and shared attention.
Neural Networks, 2006

2005
Probabilistic Gaze Imitation and Saliency Learning in a Robotic Head.
Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005


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