Matthew Hoffman

According to our database, Matthew Hoffman authored at least 73 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Scalable Spatiotemporal Prediction with Bayesian Neural Fields.

Feras Saad, Jacob Burnim, Colin Carroll, Brian Patton, Urs Köster, Rif A. Saurous, Matthew Hoffman

CoRR, 2024

Robust Inverse Graphics via Probabilistic Inference.

Tuan Anh Le, Pavel Sountsov, Matthew D. Hoffman, Ben Lee, Brian Patton, Rif A. Saurous

CoRR, 2024

2023

Training Chain-of-Thought via Latent-Variable Inference.

Matthew Douglas Hoffman, Du Phan, David Dohan, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, Rif A. Saurous

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sequential Monte Carlo Learning for Time Series Structure Discovery.

Feras Saad, Brian Patton, Matthew Douglas Hoffman, Rif A. Saurous, Vikash Mansinghka

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Underspecification Presents Challenges for Credibility in Modern Machine Learning.

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

J. Mach. Learn. Res., 2022

Lossy Compression with Gaussian Diffusion.

Lucas Theis, Tim Salimans, Matthew D. Hoffman, Fabian Mentzer

CoRR, 2022

Tuning-Free Generalized Hamiltonian Monte Carlo.

Matthew D. Hoffman, Pavel Sountsov

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Evaluating Approximate Inference in Bayesian Deep Learning.

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

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

What Are Bayesian Neural Network Posteriors Really Like?

Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson

Proceedings of the 38th International Conference on Machine Learning, 2021

An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo.

Matthew Hoffman, Alexey Radul, Pavel Sountsov

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

tfp.mcmc: Modern Markov Chain Monte Carlo Tools Built for Modern Hardware.

Junpeng Lao, Christopher Suter, Ian Langmore, Cyril Chimisov, Ashish Saxena, Pavel Sountsov, Dave Moore, Rif A. Saurous, Matthew D. Hoffman, Joshua V. Dillon

CoRR, 2020

Automatically batching control-intensive programs for modern accelerators.

Alexey Radul, Brian Patton, Dougal Maclaurin, Matthew D. Hoffman, Rif A. Saurous

Proceedings of Machine Learning and Systems 2020, 2020

Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics.

Matthew D. Hoffman, Yian Ma

Proceedings of the 37th International Conference on Machine Learning, 2020

Automatic Reparameterisation of Probabilistic Programs.

Maria I. Gorinova, Dave Moore, Matthew D. Hoffman

Proceedings of the 37th International Conference on Machine Learning, 2020

Hamiltonian Monte Carlo Swindles.

Dan Piponi, Matthew D. Hoffman, Pavel Sountsov

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

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

Proceedings of the 7th International Conference on Learning Representations, 2019

The LORACs Prior for VAEs: Letting the Trees Speak for the Data.

Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Characterizing User Skills from Application Usage Traces with Hierarchical Attention Recurrent Networks.

Longqi Yang, Chen Fang, Hailin Jin, Matthew D. Hoffman, Deborah Estrin

ACM Trans. Intell. Syst. Technol., 2018

Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language.

Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran

CoRR, 2018

Simple, Distributed, and Accelerated Probabilistic Programming.

Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew J. Johnson, Rif A. Saurous

CoRR, 2018

An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation.

Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Curtis Hawthorne, Andrew M. Dai, Matthew D. Hoffman, Douglas Eck

CoRR, 2018

Variational Autoencoders for Collaborative Filtering.

Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara

Proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018

Simple, Distributed, and Accelerated Probabilistic Programming.

Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language.

Matthew D. Hoffman

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Generalizing Hamiltonian Monte Carlo with Neural Networks.

Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein

Proceedings of the 6th International Conference on Learning Representations, 2018

Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models.

Jesse H. Engel, Matthew D. Hoffman, Adam Roberts

Proceedings of the 6th International Conference on Learning Representations, 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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

On the challenges of learning with inference networks on sparse, high-dimensional data.

Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths.

Zhicheng Liu, Yang Wang, Mira Dontcheva, Matthew Hoffman, Seth Walker, Alan Wilson

IEEE Trans. Vis. Comput. Graph., 2017

Stochastic Gradient Descent as Approximate Bayesian Inference.

Stephan Mandt, Matthew D. Hoffman, David M. Blei

J. Mach. Learn. Res., 2017

TensorFlow Distributions.

Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matthew D. Hoffman, Rif A. Saurous

CoRR, 2017

CoreFlow: Extracting and Visualizing Branching Patterns from Event Sequences.

Zhicheng Liu, Bernard Kerr, Mira Dontcheva, Justin Grover, Matthew Hoffman, Alan Wilson

Comput. Graph. Forum, 2017

Personalizing Software and Web Services by Integrating Unstructured Application Usage Traces.

Longqi Yang, Chen Fang, Hailin Jin, Matthew D. Hoffman, Deborah Estrin

Proceedings of the 26th International Conference on World Wide Web Companion, 2017

Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo.

Matthew D. Hoffman

Proceedings of the 34th International Conference on Machine Learning, 2017

Deep Probabilistic Programming.

Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

A Joint Model for Who-to-Follow and What-to-View Recommendations on Behance.

Maja R. Rudolph, Matthew D. Hoffman, Aaron Hertzmann

Proceedings of the 25th International Conference on World Wide Web, 2016

Scalable Nonparametric Bayesian Multilevel Clustering.

Viet Huynh, Dinh Q. Phung, Svetha Venkatesh, XuanLong Nguyen, Matthew D. Hoffman, Hung Hai Bui

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM.

Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams

Proceedings of the 33nd International Conference on Machine Learning, 2016

A Variational Analysis of Stochastic Gradient Algorithms.

Stephan Mandt, Matthew D. Hoffman, David M. Blei

Proceedings of the 33nd International Conference on Machine Learning, 2016

Fast and easy crowdsourced perceptual audio evaluation.

Mark Cartwright, Bryan Pardo, Gautham J. Mysore, Matthew D. Hoffman

Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

2015

The Stan Math Library: Reverse-Mode Automatic Differentiation in C++.

Bob Carpenter, Matthew D. Hoffman, Marcus A. Brubaker, Daniel D. Lee, Peter Li, Michael Betancourt

CoRR, 2015

Learning Activation Functions to Improve Deep Neural Networks.

Forest Agostinelli, Matthew D. Hoffman, Peter J. Sadowski, Pierre Baldi

Proceedings of the 3rd International Conference on Learning Representations, 2015

Deep Classifiers from Image Tags in the Wild.

Hamid Izadinia, Bryan C. Russell, Ali Farhadi, Matthew D. Hoffman, Aaron Hertzmann

Proceedings of the 2015 Workshop on Community-Organized Multimodal Mining: Opportunities for Novel Solutions, 2015

A trust-region method for stochastic variational inference with applications to streaming data.

Lucas Theis, Matthew D. Hoffman

Proceedings of the 32nd International Conference on Machine Learning, 2015

Celeste: Variational inference for a generative model of astronomical images.

Jeffrey Regier, Andrew C. Miller, Jon McAuliffe, Ryan P. Adams, Matthew D. Hoffman, Dustin Lang, David Schlegel, Prabhat

Proceedings of the 32nd International Conference on Machine Learning, 2015

Speech dereverberation using a learned speech model.

Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore

Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

Stochastic Structured Variational Inference.

Matthew D. Hoffman, David M. Blei

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Static and Dynamic Source Separation Using Nonnegative Factorizations: A unified view.

Paris Smaragdis, Cédric Févotte, Gautham J. Mysore, Nasser Mohammadiha, Matthew D. Hoffman

IEEE Signal Process. Mag., 2014

The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.

Matthew D. Hoffman, Andrew Gelman

J. Mach. Learn. Res., 2014

A Generative Product-of-Filters Model of Audio.

Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore

Proceedings of the 2nd International Conference on Learning Representations, 2014

Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference.

Dawen Liang, Matthew D. Hoffman

CoRR, 2014

Image Classification and Retrieval from User-Supplied Tags.

Hamid Izadinia, Ali Farhadi, Aaron Hertzmann, Matthew D. Hoffman

CoRR, 2014

Stochastic Structured Mean-Field Variational Inference.

Matthew D. Hoffman

CoRR, 2014

Speech decoloration based on the product-of-filters model.

Dawen Liang, Daniel P. W. Ellis, Matthew D. Hoffman, Gautham J. Mysore

Proceedings of the IEEE International Conference on Acoustics, 2014

Exploiting long-term temporal dependencies in NMF using recurrent neural networks with application to source separation.

Nicolas Boulanger-Lewandowski, Gautham J. Mysore, Matthew D. Hoffman

Proceedings of the IEEE International Conference on Acoustics, 2014

A problem with (and fix for) variational Bayesian NMF.

Matthew D. Hoffman

Proceedings of the 2014 IEEE Global Conference on Signal and Information Processing, 2014

2013

Stochastic variational inference.

Matthew D. Hoffman, David M. Blei, Chong Wang, John W. Paisley

J. Mach. Learn. Res., 2013

Beta Process Sparse Nonnegative Matrix Factorization for Music.

Dawen Liang, Matthew D. Hoffman, Daniel P. W. Ellis

Proceedings of the 14th International Society for Music Information Retrieval Conference, 2013

2012

Sparse stochastic inference for latent Dirichlet allocation.

David M. Mimno, Matthew D. Hoffman, David M. Blei

Proceedings of the 29th International Conference on Machine Learning, 2012

Nonparametric variational inference.

Samuel Gershman, Matthew D. Hoffman, David M. Blei

Proceedings of the 29th International Conference on Machine Learning, 2012

Poisson-uniform nonnegative matrix factorization.

Matthew D. Hoffman

Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

2010

Approximate Maximum A Posteriori Inference with Entropic Priors

Matthew D. Hoffman

CoRR, 2010

Online Learning for Latent Dirichlet Allocation.

Matthew D. Hoffman, David M. Blei, Francis R. Bach

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Bayesian Nonparametric Matrix Factorization for Recorded Music.

Matthew D. Hoffman, David M. Blei, Perry R. Cook

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2009

Easy As CBA: A Simple Probabilistic Model for Tagging Music.

Matthew D. Hoffman, David M. Blei, Perry R. Cook

Proceedings of the 10th International Society for Music Information Retrieval Conference, 2009

Bayesian Spectral Matching: Turning Young MC into MC Hammer via MCMC Sampling.

Matthew D. Hoffman, Perry R. Cook, David M. Blei

Proceedings of the 2009 International Computer Music Conference, 2009

2008

Content-Based Musical Similarity Computation using the Hierarchical Dirichlet Process.

Matthew D. Hoffman, David M. Blei, Perry R. Cook

Proceedings of the ISMIR 2008, 2008

Data-Driven Recomposition using the Hierarchical Dirichlet Process Hidden Markov Model.

Matthew D. Hoffman, Perry R. Cook, David M. Blei

Proceedings of the 2008 International Computer Music Conference, 2008

2007

Real-Time Feature-Based Synthesis for Live Musical Performance.

Matthew D. Hoffman, Perry R. Cook

Proceedings of the Seventh International Conference on New Interfaces for Musical Expression, 2007

The Featsynth Framework for Feature-Based synthesis: Design and Applications.

Matthew D. Hoffman, Perry R. Cook

Proceedings of the 2007 International Computer Music Conference, 2007

2006

Feature-Based Synthesis: A Tool for Evaluating, Designing, and Interacting with Music IR Systems.

Matthew D. Hoffman, Perry R. Cook

Proceedings of the ISMIR 2006, 2006

Feature-Based Synthesis: Mapping Acoustic and Perceptual Features onto Synthesis Parameters.

Matthew D. Hoffman, Perry R. Cook

Proceedings of the 2006 International Computer Music Conference, 2006