Matthew D. Hoffman

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 51

Top Topics

Negative Matrix Factorization

Stochastic Gradient

Variational Inference

Top Venues

Publications

Tuan Anh Le, Pavel Sountsov, Matthew D. Hoffman, Ben Lee, Brian Patton, Rif A. Saurous
Robust Inverse Graphics via Probabilistic Inference. CoRR (2024)
Feras A. Saad, Brian J. Patton, Matthew D. Hoffman, Rif A. Saurous, Vikash K. Mansinghka
Sequential Monte Carlo Learning for Time Series Structure Discovery. CoRR (2023)
Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, Rif A. Saurous
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images. AISTATS (2023)
Du Phan, Matthew D. Hoffman, David Dohan, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, Rif A. Saurous
Training Chain-of-Thought via Latent-Variable Inference. CoRR (2023)
Lucas Theis, Tim Salimans, Matthew D. Hoffman, Fabian Mentzer
Lossy Compression with Gaussian Diffusion. CoRR (2022)
Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, Rif A. Saurous
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images. CoRR (2022)
Matthew D. Hoffman, Pavel Sountsov
Tuning-Free Generalized Hamiltonian Monte Carlo. AISTATS (2022)
Alexander D'Amour, Katherine A. 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, Yi-An Ma, Cory Y. 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
Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 23 (2022)
Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson
What Are Bayesian Neural Network Posteriors Really Like? CoRR (2021)
Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson
What Are Bayesian Neural Network Posteriors Really Like? ICML (2021)
Andrew Gordon Wilson, Pavel Izmailov, Matthew D. Hoffman, Yarin Gal, Yingzhen Li, Melanie F. Pradier, Sharad Vikram, Andrew Y. K. Foong, Sanae Lotfi, Sebastian Farquhar
Evaluating Approximate Inference in Bayesian Deep Learning. NeurIPS (Competition and Demos) (2021)
Alexander D'Amour, Katherine A. 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, Yi-An Ma, Cory Y. 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
Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR (2020)
Junpeng Lao, Christopher Suter, Ian Langmore, Cyril Chimisov, Ashish Saxena, Pavel Sountsov, Dave Moore, Rif A. Saurous, Matthew D. Hoffman, Joshua V. Dillon
tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware. CoRR (2020)
Dan Piponi, Matthew D. Hoffman, Pavel Sountsov
Hamiltonian Monte Carlo Swindles. AISTATS (2020)
Matthew D. Hoffman, Yian Ma
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics. ICML (2020)
Maria I. Gorinova, Dave Moore, Matthew D. Hoffman
Automatic Reparameterisation of Probabilistic Programs. ICML (2020)
Alexey Radul, Brian Patton, Dougal Maclaurin, Matthew D. Hoffman, Rif A. Saurous
Automatically batching control-intensive programs for modern accelerators. MLSys (2020)
Alexey Radul, Brian Patton, Dougal Maclaurin, Matthew D. Hoffman, Rif A. Saurous
Automatically Batching Control-Intensive Programs for Modern Accelerators. CoRR (2019)
Maria I. Gorinova, Dave Moore, Matthew D. Hoffman
Automatic Reparameterisation of Probabilistic Programs. CoRR (2019)
Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson
The LORACs Prior for VAEs: Letting the Trees Speak for the Data. AISTATS (2019)
Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Ian Simon, Curtis Hawthorne, Noam Shazeer, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, Douglas Eck
Music Transformer: Generating Music with Long-Term Structure. ICLR (Poster) (2019)
Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul
Simple, Distributed, and Accelerated Probabilistic Programming. NeurIPS (2018)
Jesse H. Engel, Matthew D. Hoffman, Adam Roberts
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models. ICLR (Poster) (2018)
Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein
Generalizing Hamiltonian Monte Carlo with Neural Networks. ICLR (Poster) (2018)
Matthew D. Hoffman
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language. NeurIPS (2018)
Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman
On the challenges of learning with inference networks on sparse, high-dimensional data. AISTATS (2018)
Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson
The LORACs prior for VAEs: Letting the Trees Speak for the Data. CoRR (2018)
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
Variational Autoencoders for Collaborative Filtering. CoRR (2018)
Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Curtis Hawthorne, Andrew M. Dai, Matthew D. Hoffman, Douglas Eck
An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation. CoRR (2018)
Longqi Yang, Chen Fang, Hailin Jin, Matthew D. Hoffman, Deborah Estrin
Characterizing User Skills from Application Usage Traces with Hierarchical Attention Recurrent Networks. ACM Trans. Intell. Syst. Technol. 9 (6) (2018)
Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew J. Johnson, Rif A. Saurous
Simple, Distributed, and Accelerated Probabilistic Programming. CoRR (2018)
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
Variational Autoencoders for Collaborative Filtering. WWW (2018)
Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. AISTATS (2018)
Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language. CoRR (2018)
Stephan Mandt, Matthew D. Hoffman, David M. Blei
Stochastic Gradient Descent as Approximate Bayesian Inference. CoRR (2017)
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei
Deep Probabilistic Programming. ICLR (Poster) (2017)
Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matthew D. Hoffman, Rif A. Saurous
TensorFlow Distributions. CoRR (2017)
Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein
Generalizing Hamiltonian Monte Carlo with Neural Networks. CoRR (2017)
Jesse H. Engel, Matthew D. Hoffman, Adam Roberts
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models. CoRR (2017)
Longqi Yang, Chen Fang, Hailin Jin, Matthew D. Hoffman, Deborah Estrin
Personalizing Software and Web Services by Integrating Unstructured Application Usage Traces. WWW (Companion Volume) (2017)
Stephan Mandt, Matthew D. Hoffman, David M. Blei
Stochastic Gradient Descent as Approximate Bayesian Inference. J. Mach. Learn. Res. 18 (2017)
Matthew D. Hoffman
Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo. ICML (2017)
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei
Deep Probabilistic Programming. CoRR (2017)
Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman
On the challenges of learning with inference networks on sparse, high-dimensional data. CoRR (2017)
Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. CoRR (2017)
Mark Cartwright, Bryan Pardo, Gautham J. Mysore, Matthew D. Hoffman
Fast and easy crowdsourced perceptual audio evaluation. ICASSP (2016)
Stephan Mandt, Matthew D. Hoffman, David M. Blei
A Variational Analysis of Stochastic Gradient Algorithms. CoRR (2016)
Viet Huynh, Dinh Q. Phung, Svetha Venkatesh, XuanLong Nguyen, Matthew D. Hoffman, Hung Hai Bui
Scalable Nonparametric Bayesian Multilevel Clustering. UAI (2016)
Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. ICML (2016)
Maja R. Rudolph, Matthew D. Hoffman, Aaron Hertzmann
A Joint Model for Who-to-Follow and What-to-View Recommendations on Behance. WWW (Companion Volume) (2016)
Stephan Mandt, Matthew D. Hoffman, David M. Blei
A Variational Analysis of Stochastic Gradient Algorithms. ICML (2016)
Lucas Theis, Matthew D. Hoffman
A trust-region method for stochastic variational inference with applications to streaming data. ICML (2015)
Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore
Speech dereverberation using a learned speech model. ICASSP (2015)
Matthew D. Hoffman, David M. Blei
Stochastic Structured Variational Inference. AISTATS (2015)
Hamid Izadinia, Bryan C. Russell, Ali Farhadi, Matthew D. Hoffman, Aaron Hertzmann
Deep Classifiers from Image Tags in the Wild. MMCommons@ACM Multimedia (2015)
Forest Agostinelli, Matthew D. Hoffman, Peter J. Sadowski, Pierre Baldi
Learning Activation Functions to Improve Deep Neural Networks. ICLR (Workshop) (2015)
Bob Carpenter, Matthew D. Hoffman, Marcus Brubaker, Daniel D. Lee, Peter Li, Michael Betancourt
The Stan Math Library: Reverse-Mode Automatic Differentiation in C++. CoRR (2015)
Jeffrey Regier, Andrew C. Miller, Jon McAuliffe, Ryan P. Adams, Matthew D. Hoffman, Dustin Lang, David Schlegel, Prabhat
Celeste: Variational inference for a generative model of astronomical images. ICML (2015)
Dawen Liang, Matthew D. Hoffman
Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference. CoRR (2014)
Paris Smaragdis, Cédric Févotte, Gautham J. Mysore, Nasser Mohammadiha, Matthew D. Hoffman
Static and Dynamic Source Separation Using Nonnegative Factorizations: A unified view. IEEE Signal Process. Mag. 31 (3) (2014)
Dawen Liang, Daniel P. W. Ellis, Matthew D. Hoffman, Gautham J. Mysore
Speech decoloration based on the product-of-filters model. ICASSP (2014)
Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore
A Generative Product-of-Filters Model of Audio. ICLR (Poster) (2014)
Matthew D. Hoffman
Stochastic Structured Mean-Field Variational Inference. CoRR (2014)
Hamid Izadinia, Ali Farhadi, Aaron Hertzmann, Matthew D. Hoffman
Image Classification and Retrieval from User-Supplied Tags. CoRR (2014)
Nicolas Boulanger-Lewandowski, Gautham J. Mysore, Matthew D. Hoffman
Exploiting long-term temporal dependencies in NMF using recurrent neural networks with application to source separation. ICASSP (2014)
Matthew D. Hoffman, Andrew Gelman
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15 (1) (2014)
Matthew D. Hoffman
A problem with (and fix for) variational Bayesian NMF. GlobalSIP (2014)
Matthew D. Hoffman, David M. Blei, Chong Wang, John W. Paisley
Stochastic variational inference. J. Mach. Learn. Res. 14 (1) (2013)
Dawen Liang, Matthew D. Hoffman, Daniel P. W. Ellis
Beta Process Sparse Nonnegative Matrix Factorization for Music. ISMIR (2013)
Samuel Gershman, Matthew D. Hoffman, David M. Blei
Nonparametric variational inference. ICML (2012)
David M. Mimno, Matthew D. Hoffman, David M. Blei
Sparse stochastic inference for latent Dirichlet allocation. ICML (2012)
Matthew D. Hoffman, David M. Blei, Chong Wang, John W. Paisley
Stochastic Variational Inference. CoRR (2012)
Samuel Gershman, Matthew D. Hoffman, David M. Blei
Nonparametric variational inference CoRR (2012)
Matthew D. Hoffman
Poisson-uniform nonnegative matrix factorization. ICASSP (2012)
Matthew D. Hoffman, Andrew Gelman
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo CoRR (2011)
Matthew D. Hoffman, David M. Blei, Perry R. Cook
Bayesian Nonparametric Matrix Factorization for Recorded Music. ICML (2010)
Matthew D. Hoffman
Approximate Maximum A Posteriori Inference with Entropic Priors CoRR (2010)
Matthew D. Hoffman, David M. Blei, Francis R. Bach
Online Learning for Latent Dirichlet Allocation. NIPS (2010)
Matthew D. Hoffman, David M. Blei, Perry R. Cook
Easy As CBA: A Simple Probabilistic Model for Tagging Music. ISMIR (2009)
Matthew D. Hoffman, Perry R. Cook, David M. Blei
Bayesian Spectral Matching: Turning Young MC into MC Hammer via MCMC Sampling. ICMC (2009)
Matthew D. Hoffman, Perry R. Cook, David M. Blei
Data-Driven Recomposition using the Hierarchical Dirichlet Process Hidden Markov Model. ICMC (2008)
Matthew D. Hoffman, David M. Blei, Perry R. Cook
Content-Based Musical Similarity Computation using the Hierarchical Dirichlet Process. ISMIR (2008)
Matthew D. Hoffman, Perry R. Cook
The Featsynth Framework for Feature-Based synthesis: Design and Applications. ICMC (2007)
Matthew D. Hoffman, Perry R. Cook
Real-Time Feature-Based Synthesis for Live Musical Performance. NIME (2007)
Matthew D. Hoffman, Perry R. Cook
Feature-Based Synthesis: Mapping Acoustic and Perceptual Features onto Synthesis Parameters. ICMC (2006)
Matthew D. Hoffman, Perry R. Cook
Feature-Based Synthesis: A Tool for Evaluating, Designing, and Interacting with Music IR Systems. ISMIR (2006)