Hoffman, Matthew D.

22 publications

AISTATS 2023 ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, Rif A. Saurous

AISTATS 2022 Tuning-Free Generalized Hamiltonian Monte Carlo Matthew D. Hoffman, Pavel Sountsov

JMLR 2022 Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley

ICML 2021 What Are Bayesian Neural Network Posteriors Really like? Pavel Izmailov, Sharad Vikram, Matthew D Hoffman, Andrew Gordon Gordon Wilson

ICLR 2019 Music Transformer: Generating Music with Long-Term Structure Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Ian Simon, Curtis Hawthorne, Noam Shazeer, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, Douglas Eck

AISTATS 2019 The LORACs Prior for VAEs: Letting the Trees Speak for the Data Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson

NeurIPSW 2019 Transforming Recursive Programs for Parallel Execution Alexey Radul, Brian Patton, Dougal Maclaurin, Matthew D. Hoffman, Rif A. Saurous

NeurIPS 2018 Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language Matthew D. Hoffman, Matthew J Johnson, Dustin Tran

AISTATS 2018 Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams

AISTATS 2018 On the Challenges of Learning with Inference Networks on Sparse, High-Dimensional Data Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman

ICLR 2017 Deep Probabilistic Programming Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei

ICML 2017 Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo Matthew D. Hoffman

JMLR 2017 Stochastic Gradient Descent as Approximate Bayesian Inference Stephan Mandt, Matthew D. Hoffman, David M. Blei

UAI 2016 Scalable Nonparametric Bayesian Multilevel Clustering Viet Huynh, Dinh Q. Phung, Svetha Venkatesh, XuanLong Nguyen, Matthew D. Hoffman, Hung Hai Bui

ICLR 2015 Learning Activation Functions to Improve Deep Neural Networks Forest Agostinelli, Matthew D. Hoffman, Peter J. Sadowski, Pierre Baldi

AISTATS 2015 Stochastic Structured Variational Inference Matthew D. Hoffman, David M. Blei

ICLR 2014 A Generative Product-of-Filters Model of Audio Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore

JMLR 2014 The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman, Andrew Gelman

JMLR 2013 Stochastic Variational Inference Matthew D. Hoffman, David M. Blei, Chong Wang, John Paisley

ICML 2012 Nonparametric Variational Inference Samuel Gershman, Matthew D. Hoffman, David M. Blei

ICML 2012 Sparse Stochastic Inference for Latent Dirichlet Allocation David M. Mimno, Matthew D. Hoffman, David M. Blei

ICML 2010 Bayesian Nonparametric Matrix Factorization for Recorded Music Matthew D. Hoffman, David M. Blei, Perry R. Cook