| 2023 | AISTATS | 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 |
| 2022 | AISTATS | Tuning-Free Generalized Hamiltonian Monte Carlo. | Matthew D. Hoffman, Pavel Sountsov |
| 2021 | ICML | What Are Bayesian Neural Network Posteriors Really Like? | Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson |
| 2020 | AISTATS | Hamiltonian Monte Carlo Swindles. | Dan Piponi, Matthew D. Hoffman, Pavel Sountsov |
| 2020 | ICML | Automatic Reparameterisation of Probabilistic Programs. | Maria I. Gorinova, Dave Moore, Matthew D. Hoffman |
| 2020 | ICML | Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics. | Matthew D. Hoffman, Yian Ma |
| 2019 | AISTATS | The LORACs Prior for VAEs: Letting the Trees Speak for the Data. | Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson |
| 2019 | ICLR | 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 |
| 2018 | AISTATS | On the challenges of learning with inference networks on sparse, high-dimensional data. | Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman |
| 2018 | AISTATS | 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 |
| 2018 | ICLR | Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models. | Jesse H. Engel, Matthew D. Hoffman, Adam Roberts |
| 2018 | ICLR | Generalizing Hamiltonian Monte Carlo with Neural Networks. | Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein |
| 2018 | WWW | Variational Autoencoders for Collaborative Filtering. | Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara |
| 2017 | ICLR | Deep Probabilistic Programming. | Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei |
| 2017 | ICML | Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo. | Matthew D. Hoffman |
| 2017 | WWW | Personalizing Software and Web Services by Integrating Unstructured Application Usage Traces. | Longqi Yang, Chen Fang, Hailin Jin, Matthew D. Hoffman, Deborah Estrin |
| 2016 | ICASSP | Fast and easy crowdsourced perceptual audio evaluation. | Mark Cartwright, Bryan Pardo, Gautham J. Mysore, Matthew D. Hoffman |
| 2016 | ICML | A Variational Analysis of Stochastic Gradient Algorithms. | Stephan Mandt, Matthew D. Hoffman, David M. Blei |
| 2016 | ICML | The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. | Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams |
| 2016 | WWW | A Joint Model for Who-to-Follow and What-to-View Recommendations on Behance. | Maja R. Rudolph, Matthew D. Hoffman, Aaron Hertzmann |
| 2016 | UAI | Scalable Nonparametric Bayesian Multilevel Clustering. | Viet Huynh, Dinh Q. Phung, Svetha Venkatesh, XuanLong Nguyen, Matthew D. Hoffman, Hung Hai Bui |
| 2015 | AISTATS | Stochastic Structured Variational Inference. | Matthew D. Hoffman, David M. Blei |
| 2015 | ICASSP | Speech dereverberation using a learned speech model. | Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore |
| 2015 | ICML | 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 |
| 2015 | ICML | A trust-region method for stochastic variational inference with applications to streaming data. | Lucas Theis, Matthew D. Hoffman |
| 2014 | ICASSP | 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 |
| 2014 | ICASSP | Speech decoloration based on the product-of-filters model. | Dawen Liang, Daniel P. W. Ellis, Matthew D. Hoffman, Gautham J. Mysore |
| 2012 | ICASSP | Poisson-uniform nonnegative matrix factorization. | Matthew D. Hoffman |
| 2012 | ICML | Nonparametric variational inference. | Samuel Gershman, Matthew D. Hoffman, David M. Blei |
| 2012 | ICML | Sparse stochastic inference for latent Dirichlet allocation. | David M. Mimno, Matthew D. Hoffman, David M. Blei |
| 2010 | ICML | Bayesian Nonparametric Matrix Factorization for Recorded Music. | Matthew D. Hoffman, David M. Blei, Perry R. Cook |