Matthew D. Hoffman

86 papers A* 14A 8Misc 5Journal 37Unranked 22

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