Hoffman, Matthew

18 publications

ICLR 2025 BOND: Aligning LLMs with Best-of-N Distillation Pier Giuseppe Sessa, Robert Dadashi-Tazehozi, Leonard Hussenot, Johan Ferret, Nino Vieillard, Alexandre Rame, Bobak Shahriari, Sarah Perrin, Abram L. Friesen, Geoffrey Cideron, Sertan Girgin, Piotr Stanczyk, Andrea Michi, Danila Sinopalnikov, Sabela Ramos Garea, Amélie Héliou, Aliaksei Severyn, Matthew Hoffman, Nikola Momchev, Olivier Bachem

ICLRW 2023 Knowledge Transfer from Teachers to Learners in Growing-Batch Reinforcement Learning Patrick Emedom-Nnamdi, Abram L. Friesen, Bobak Shahriari, Nando de Freitas, Matthew Hoffman

WACV 2023 Semantic Segmentation with Active Semi-Supervised Learning Aneesh Rangnekar, Christopher Kanan, Matthew Hoffman

TMLR 2022 An Empirical Study of Implicit Regularization in Deep Offline RL Caglar Gulcehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matthew Hoffman, Razvan Pascanu, Arnaud Doucet

AISTATS 2021 An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo Matthew Hoffman, Alexey Radul, Pavel Sountsov

ICML 2020 Automatic Reparameterisation of Probabilistic Programs Maria Gorinova, Dave Moore, Matthew Hoffman

ICML 2020 Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics Matthew Hoffman, Yian Ma

AISTATS 2020 Hamiltonian Monte Carlo Swindles Dan Piponi, Matthew Hoffman, Pavel Sountsov

NeurIPS 2020 Modular Meta-Learning with Shrinkage Yutian Chen, Abram L. Friesen, Feryal Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas

NeurIPS 2020 RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez, Konrad Zolna, Rishabh Agarwal, Josh S Merel, Daniel J Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas

ICLR 2018 Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models Jesse Engel, Matthew Hoffman, Adam Roberts

ICML 2016 A Variational Analysis of Stochastic Gradient Algorithms Stephan Mandt, Matthew Hoffman, David Blei

ICML 2016 The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM Ardavan Saeedi, Matthew Hoffman, Matthew Johnson, Ryan Adams

ICML 2015 Predictive Entropy Search for Bayesian Optimization with Unknown Constraints Jose Miguel Hernandez-Lobato, Michael Gelbart, Matthew Hoffman, Ryan Adams, Zoubin Ghahramani

UAI 2011 Portfolio Allocation for Bayesian Optimization Matthew Hoffman, Eric Brochu, Nando de Freitas

NeurIPS 2010 Online Learning for Latent Dirichlet Allocation Matthew Hoffman, Francis R. Bach, David M. Blei

AISTATS 2009 An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward Matthew Hoffman, Nando Freitas, Arnaud Doucet, Jan Peters

NeurIPS 2007 Bayesian Policy Learning with Trans-Dimensional MCMC Matthew Hoffman, Arnaud Doucet, Nando D. Freitas, Ajay Jasra