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

Feb 04, 2020
Junpeng Lao, Christopher Suter, Ian Langmore, Cyril Chimisov, Ashish Saxena, Pavel Sountsov, Dave Moore, Rif A. Saurous, Matthew D. Hoffman, Joshua V. Dillon

* Based on extended abstract submitted to PROBPROG 2020 

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Automatically Batching Control-Intensive Programs for Modern Accelerators

Oct 23, 2019
Alexey Radul, Brian Patton, Dougal Maclaurin, Matthew D. Hoffman, Rif A. Saurous

* 10 pages; under review for Systems and Machine Learning 2020 

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Automatic Reparameterisation of Probabilistic Programs

Jun 07, 2019
Maria I. Gorinova, Dave Moore, Matthew D. Hoffman


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

Nov 29, 2018
Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran

* Appears in Neural Information Processing Systems, 2018. Code available at https://github.com/google-research/autoconj 

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The LORACs prior for VAEs: Letting the Trees Speak for the Data

Oct 16, 2018
Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson


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Music Transformer

Oct 10, 2018
Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Ian Simon, Curtis Hawthorne, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, Douglas Eck

* Rewrote many sections to clarify the work, and extended relative attention to the local case. Previous title is "An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation" 

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Generalizing Hamiltonian Monte Carlo with Neural Networks

Mar 02, 2018
Daniel Levy, Matthew D. Hoffman, Jascha Sohl-Dickstein

* ICLR 2018 

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Variational Autoencoders for Collaborative Filtering

Feb 16, 2018
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara

* 10 pages, 3 figures. WWW 2018 

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Stochastic Gradient Descent as Approximate Bayesian Inference

Jan 19, 2018
Stephan Mandt, Matthew D. Hoffman, David M. Blei

* Journal of Machine Learning Research 18 (2017) 1-35 
* 35 pages, published version (JMLR 2017) 

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Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models

Apr 17, 2017
Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams


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Deep Probabilistic Programming

Mar 07, 2017
Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei

* Appears in International Conference on Learning Representations, 2017. A companion webpage for this paper is available at http://edwardlib.org/iclr2017 

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A Variational Analysis of Stochastic Gradient Algorithms

Feb 08, 2016
Stephan Mandt, Matthew D. Hoffman, David M. Blei

* International Conference on Machine Learning (ICML 2016), p. 354--363 
* 8 pages, 3 figures 

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

May 28, 2015
Lucas Theis, Matthew D. Hoffman

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

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

Dec 02, 2014
Dawen Liang, Matthew D. Hoffman

* 6 pages, 1 figure 

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Structured Stochastic Variational Inference

Nov 26, 2014
Matthew D. Hoffman, David M. Blei


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A Generative Product-of-Filters Model of Audio

Nov 25, 2014
Dawen Liang, Matthew D. Hoffman, Gautham J. Mysore

* ICLR 2014 conference-track submission. Added link to the source code 

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Image Classification and Retrieval from User-Supplied Tags

Nov 25, 2014
Hamid Izadinia, Ali Farhadi, Aaron Hertzmann, Matthew D. Hoffman


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The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

Nov 18, 2011
Matthew D. Hoffman, Andrew Gelman

* 30 pages, 7 figures 

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Approximate Maximum A Posteriori Inference with Entropic Priors

Sep 29, 2010
Matthew D. Hoffman


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