Hoffman, Matthew W.

10 publications

ICLR 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 Lillicrap

NeurIPS 2018 Simple, Distributed, and Accelerated Probabilistic Programming Dustin Tran, Matthew W Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul

ICML 2017 Learned Optimizers That Scale and Generalize Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gómez Colmenarejo, Misha Denil, Nando Freitas, Jascha Sohl-Dickstein

ICML 2017 Learning to Learn Without Gradient Descent by Gradient Descent Yutian Chen, Matthew W. Hoffman, Sergio Gómez Colmenarejo, Misha Denil, Timothy P. Lillicrap, Matt Botvinick, Nando Freitas

CoRL 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

JMLR 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

NeurIPS 2016 Learning to Learn by Gradient Descent by Gradient Descent Marcin Andrychowicz, Misha Denil, Sergio Gómez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas

AISTATS 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

NeurIPS 2014 Predictive Entropy Search for Efficient Global Optimization of Black-Box Functions José Miguel Hernández-Lobato, Matthew W Hoffman, Zoubin Ghahramani

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