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Matthew Hoffman 0001
Matthew D. Hoffman – Matthew Douglas Hoffman
Person information
- affiliation: Adobe Research
- affiliation: Columbia University, New York, Department of Statistics
- affiliation: Princeton University, Department of Computer Science
Other persons with the same name
- Matt Hoffman 0001 (aka: Matthew Hoffman 0002, Matthew W. Hoffman) — Google DeepMind, London, UK (and 2 more)
- Matthew Hoffman 0003 — National Museum of American History, Smithsonian Institution, Washington, DC, USA
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2020 – today
- 2024
- [c53]Tuan Anh Le, Pavel Sountsov, Matthew Douglas Hoffman, Ben Lee, Brian Patton, Rif A. Saurous:
Robust Inverse Graphics via Probabilistic Inference. ICML 2024 - [i32]Tuan Anh Le, Pavel Sountsov, Matthew D. Hoffman, Ben Lee, Brian Patton, Rif A. Saurous:
Robust Inverse Graphics via Probabilistic Inference. CoRR abs/2402.01915 (2024) - [i31]Feras Saad, Jacob Burnim, Colin Carroll, Brian Patton, Urs Köster, Rif A. Saurous, Matthew Hoffman:
Scalable Spatiotemporal Prediction with Bayesian Neural Fields. CoRR abs/2403.07657 (2024) - 2023
- [c52]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: 10425-10444 - [c51]Feras Saad, Brian Patton, Matthew Douglas Hoffman, Rif A. Saurous, Vikash Mansinghka:
Sequential Monte Carlo Learning for Time Series Structure Discovery. ICML 2023: 29473-29489 - [c50]Matthew Douglas Hoffman, Du Phan, 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. NeurIPS 2023 - [i30]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 abs/2307.09607 (2023) - [i29]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 abs/2312.02179 (2023) - 2022
- [j8]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: 226:1-226:61 (2022) - [c49]Matthew D. Hoffman, Pavel Sountsov:
Tuning-Free Generalized Hamiltonian Monte Carlo. AISTATS 2022: 7799-7813 - [i28]Lucas Theis, Tim Salimans, Matthew D. Hoffman, Fabian Mentzer:
Lossy Compression with Gaussian Diffusion. CoRR abs/2206.08889 (2022) - [i27]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 abs/2210.17415 (2022) - 2021
- [c48]Matthew Hoffman, Alexey Radul, Pavel Sountsov:
An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo. AISTATS 2021: 3907-3915 - [c47]Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson:
What Are Bayesian Neural Network Posteriors Really Like? ICML 2021: 4629-4640 - [c46]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: 113-124 - [i26]Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson:
What Are Bayesian Neural Network Posteriors Really Like? CoRR abs/2104.14421 (2021) - 2020
- [c45]Dan Piponi, Matthew D. Hoffman, Pavel Sountsov:
Hamiltonian Monte Carlo Swindles. AISTATS 2020: 3774-3783 - [c44]Maria I. Gorinova, Dave Moore, Matthew D. Hoffman:
Automatic Reparameterisation of Probabilistic Programs. ICML 2020: 3648-3657 - [c43]Matthew D. Hoffman, Yian Ma:
Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics. ICML 2020: 4324-4341 - [c42]Alexey Radul, Brian Patton, Dougal Maclaurin, Matthew D. Hoffman, Rif A. Saurous:
Automatically batching control-intensive programs for modern accelerators. MLSys 2020 - [i25]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 abs/2002.01184 (2020) - [i24]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
Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR abs/2011.03395 (2020)
2010 – 2019
- 2019
- [c41]Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson:
The LORACs Prior for VAEs: Letting the Trees Speak for the Data. AISTATS 2019: 3292-3301 - [c40]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 - [i23]Maria I. Gorinova, Dave Moore, Matthew D. Hoffman:
Automatic Reparameterisation of Probabilistic Programs. CoRR abs/1906.03028 (2019) - [i22]Alexey Radul, Brian Patton, Dougal Maclaurin, Matthew D. Hoffman, Rif A. Saurous:
Automatically Batching Control-Intensive Programs for Modern Accelerators. CoRR abs/1910.11141 (2019) - 2018
- [j7]Longqi Yang
Characterizing User Skills from Application Usage Traces with Hierarchical Attention Recurrent Networks. ACM Trans. Intell. Syst. Technol. 9(6): 68:1-68:18 (2018) - [c39]Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman:
On the challenges of learning with inference networks on sparse, high-dimensional data. AISTATS 2018: 143-151 - [c38]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: 1309-1317 - [c37]Jesse H. Engel, Matthew D. Hoffman, Adam Roberts:
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models. ICLR (Poster) 2018 - [c36]Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein:
Generalizing Hamiltonian Monte Carlo with Neural Networks. ICLR (Poster) 2018 - [c35]Dustin Tran, Matthew D. Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul:
Simple, Distributed, and Accelerated Probabilistic Programming. NeurIPS 2018: 7609-7620 - [c34]Matthew D. Hoffman:
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language. NeurIPS 2018: 10739-10749 - [c33]Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara:
Variational Autoencoders for Collaborative Filtering. WWW 2018: 689-698 - [i21]Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara:
Variational Autoencoders for Collaborative Filtering. CoRR abs/1802.05814 (2018) - [i20]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 abs/1809.04281 (2018) - [i19]Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson:
The LORACs prior for VAEs: Letting the Trees Speak for the Data. CoRR abs/1810.06891 (2018) - [i18]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 abs/1811.02091 (2018) - [i17]Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran:
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language. CoRR abs/1811.11926 (2018) - 2017
- [j6]Zhicheng Liu
CoreFlow: Extracting and Visualizing Branching Patterns from Event Sequences. Comput. Graph. Forum 36(3): 527-538 (2017) - [j5]Stephan Mandt, Matthew D. Hoffman, David M. Blei:
Stochastic Gradient Descent as Approximate Bayesian Inference. J. Mach. Learn. Res. 18: 134:1-134:35 (2017) - [j4]Zhicheng Liu
Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths. IEEE Trans. Vis. Comput. Graph. 23(1): 321-330 (2017) - [c32]Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei:
Deep Probabilistic Programming. ICLR (Poster) 2017 - [c31]Matthew D. Hoffman:
Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo. ICML 2017: 1510-1519 - [c30]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: 485-493 - [i16]Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei:
Deep Probabilistic Programming. CoRR abs/1701.03757 (2017) - [i15]Stephan Mandt, Matthew D. Hoffman, David M. Blei:
Stochastic Gradient Descent as Approximate Bayesian Inference. CoRR abs/1704.04289 (2017) - [i14]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 abs/1704.04997 (2017) - [i13]Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman:
On the challenges of learning with inference networks on sparse, high-dimensional data. CoRR abs/1710.06085 (2017) - [i12]Jesse H. Engel, Matthew D. Hoffman, Adam Roberts:
Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models. CoRR abs/1711.05772 (2017) - [i11]Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein:
Generalizing Hamiltonian Monte Carlo with Neural Networks. CoRR abs/1711.09268 (2017) - [i10]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 abs/1711.10604 (2017) - 2016
- [c29]Mark Cartwright, Bryan Pardo, Gautham J. Mysore, Matthew D. Hoffman:
Fast and easy crowdsourced perceptual audio evaluation. ICASSP 2016: 619-623 - [c28]Stephan Mandt, Matthew D. Hoffman, David M. Blei:
A Variational Analysis of Stochastic Gradient Algorithms. ICML 2016: 354-363 - [c27]Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams:
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. ICML 2016: 2682-2691 - [c26]Viet Huynh, Dinh Q. Phung, Svetha Venkatesh, XuanLong Nguyen, Matthew D. Hoffman, Hung Hai Bui:
Scalable Nonparametric Bayesian Multilevel Clustering. UAI 2016 - [c25]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: 581-584 - [i9]Stephan Mandt, Matthew D. Hoffman, David M. Blei:
A Variational Analysis of Stochastic Gradient Algorithms. CoRR abs/1602.02666 (2016) - 2015
- [c24]Matthew D. Hoffman, David M. Blei:
Stochastic Structured Variational Inference. AISTATS 2015 - [c23]Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore:
Speech dereverberation using a learned speech model. ICASSP 2015: 1871-1875 - [c22]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: 2095-2103 - [c21]Lucas Theis, Matthew D. Hoffman:
A trust-region method for stochastic variational inference with applications to streaming data. ICML 2015: 2503-2511 - [c20]Hamid Izadinia, Bryan C. Russell, Ali Farhadi, Matthew D. Hoffman, Aaron Hertzmann
Deep Classifiers from Image Tags in the Wild. MMCommons@ACM Multimedia 2015: 13-18 - [c19]Forest Agostinelli
Learning Activation Functions to Improve Deep Neural Networks. ICLR (Workshop) 2015 - [i8]Bob Carpenter, Matthew D. Hoffman, Marcus A. Brubaker, Daniel D. Lee, Peter Li, Michael Betancourt:
The Stan Math Library: Reverse-Mode Automatic Differentiation in C++. CoRR abs/1509.07164 (2015) - 2014
- [j3]Matthew D. Hoffman, Andrew Gelman:
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res. 15(1): 1593-1623 (2014) - [j2]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): 66-75 (2014) - [c18]Matthew D. Hoffman:
A problem with (and fix for) variational Bayesian NMF. GlobalSIP 2014: 527-531 - [c17]Dawen Liang, Daniel P. W. Ellis, Matthew D. Hoffman, Gautham J. Mysore:
Speech decoloration based on the product-of-filters model. ICASSP 2014: 2400-2404 - [c16]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: 6969-6973 - [c15]Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore:
A Generative Product-of-Filters Model of Audio. ICLR (Poster) 2014 - [i7]Matthew D. Hoffman:
Stochastic Structured Mean-Field Variational Inference. CoRR abs/1404.4114 (2014) - [i6]Dawen Liang, Matthew D. Hoffman:
Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference. CoRR abs/1411.1804 (2014) - [i5]Hamid Izadinia, Ali Farhadi, Aaron Hertzmann, Matthew D. Hoffman:
Image Classification and Retrieval from User-Supplied Tags. CoRR abs/1411.6909 (2014) - 2013
- [j1]Matthew D. Hoffman, David M. Blei, Chong Wang, John W. Paisley:
Stochastic variational inference. J. Mach. Learn. Res. 14(1): 1303-1347 (2013) - [c14]Dawen Liang, Matthew D. Hoffman, Daniel P. W. Ellis:
Beta Process Sparse Nonnegative Matrix Factorization for Music. ISMIR 2013: 375-380 - 2012
- [c13]Matthew D. Hoffman:
Poisson-uniform nonnegative matrix factorization. ICASSP 2012: 5361-5364 - [c12]Samuel Gershman, Matthew D. Hoffman, David M. Blei:
Nonparametric variational inference. ICML 2012 - [c11]David M. Mimno, Matthew D. Hoffman, David M. Blei:
Sparse stochastic inference for latent Dirichlet allocation. ICML 2012 - [i4]Samuel Gershman, Matthew D. Hoffman, David M. Blei:
Nonparametric variational inference. CoRR abs/1206.4665 (2012) - [i3]Matthew D. Hoffman, David M. Blei, Chong Wang, John W. Paisley:
Stochastic Variational Inference. CoRR abs/1206.7051 (2012) - 2011
- [i2]Matthew D. Hoffman, Andrew Gelman:
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. CoRR abs/1111.4246 (2011) - 2010
- [c10]Matthew D. Hoffman, David M. Blei, Perry R. Cook:
Bayesian Nonparametric Matrix Factorization for Recorded Music. ICML 2010: 439-446 - [c9]Matthew D. Hoffman, David M. Blei, Francis R. Bach:
Online Learning for Latent Dirichlet Allocation. NIPS 2010: 856-864 - [i1]Matthew D. Hoffman:
Approximate Maximum A Posteriori Inference with Entropic Priors. CoRR abs/1009.5761 (2010)
2000 – 2009
- 2009
- [c8]Matthew D. Hoffman, Perry R. Cook, David M. Blei:
Bayesian Spectral Matching: Turning Young MC into MC Hammer via MCMC Sampling. ICMC 2009 - [c7]Matthew D. Hoffman, David M. Blei, Perry R. Cook:
Easy As CBA: A Simple Probabilistic Model for Tagging Music. ISMIR 2009: 369-374 - 2008
- [c6]Matthew D. Hoffman, Perry R. Cook, David M. Blei:
Data-Driven Recomposition using the Hierarchical Dirichlet Process Hidden Markov Model. ICMC 2008 - [c5]Matthew D. Hoffman, David M. Blei, Perry R. Cook:
Content-Based Musical Similarity Computation using the Hierarchical Dirichlet Process. ISMIR 2008: 349-354 - 2007
- [c4]Matthew D. Hoffman, Perry R. Cook:
The Featsynth Framework for Feature-Based synthesis: Design and Applications. ICMC 2007 - [c3]Matthew D. Hoffman, Perry R. Cook:
Real-Time Feature-Based Synthesis for Live Musical Performance. NIME 2007: 309-312 - 2006
- [c2]Matthew D. Hoffman, Perry R. Cook:
Feature-Based Synthesis: Mapping Acoustic and Perceptual Features onto Synthesis Parameters. ICMC 2006 - [c1]Matthew D. Hoffman, Perry R. Cook:
Feature-Based Synthesis: A Tool for Evaluating, Designing, and Interacting with Music IR Systems. ISMIR 2006: 361-362
Coauthor Index
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last updated on 2024-10-07 21:24 CEST by the dblp team
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