Necessary and sufficient conditions for high-dimensional salient feature subset recovery. - Vincent Y. F. Tan; Matthew J. Johnson 002; Alan S. Willsky (ISIT, 2010)

The Hierarchical Dirichlet Process Hidden Semi-Markov Model. - Matthew J. Johnson 002; Alan S. Willsky (UAI, 2010)

A Simple Explanation of A Spectral Algorithm for Learning Hidden Markov Models - Matthew James Johnson (CoRR, 2012)

Analyzing Hogwild Parallel Gaussian Gibbs Sampling. - Matthew J. Johnson 002; James Saunderson; Alan S. Willsky (NIPS, 2013)

Bayesian nonparametric hidden semi-Markov models. - Matthew J. Johnson 002; Alan S. Willsky (J. Mach. Learn. Res., 2013)

Stochastic Variational Inference for Bayesian Time Series Models. - Matthew James Johnson; Alan S. Willsky (ICML, 2014)

Bayesian time series models and scalable inference. - Matthew James Johnson (2014)

Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation. - Scott W. Linderman; Matthew J. Johnson 002; Ryan P. Adams (NIPS, 2015)

Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models. - Jonathan H. Huggins; Ardavan Saeedi; Matthew J. Johnson 002 (CoRR, 2015)

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. - Ardavan Saeedi; Matthew D. Hoffman; Matthew J. Johnson 002; Ryan P. Adams (ICML, 2016)

Composing graphical models with neural networks for structured representations and fast inference. - Matthew J. Johnson 002; David Duvenaud; Alexander B. Wiltschko; Ryan P. Adams; Sandeep R. Datta (NIPS, 2016)

Patterns of Scalable Bayesian Inference. - Elaine Angelino; Matthew James Johnson; Ryan P. Adams (Found. Trends Mach. Learn., 2016)

Cross-Corpora Unsupervised Learning of Trajectories in Autism Spectrum Disorders. - Huseyin Melih Elibol; Vincent Nguyen; Scott W. Linderman; Matthew J. Johnson 002; Amna Hashmi; Finale Doshi-Velez (J. Mach. Learn. Res., 2016)

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. - Scott W. Linderman; Matthew J. Johnson 002; Andrew C. Miller; Ryan P. Adams; David M. Blei; Liam Paninski (AISTATS, 2017)

Structure-Exploiting variational inference for recurrent switching linear dynamical systems. - Scott W. Linderman; Matthew J. Johnson 002 (CAMSAP, 2017)

Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. - Ardavan Saeedi; Matthew D. Hoffman; Stephen J. DiVerdi; Asma Ghandeharioun; Matthew J. Johnson 002; Ryan P. Adams (AISTATS, 2018)

SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning. - Marvin Zhang; Sharad Vikram; Laura M. Smith; Pieter Abbeel; Matthew J. Johnson 002; Sergey Levine (CoRR, 2018)

Simple, Distributed, and Accelerated Probabilistic Programming. - Dustin Tran; Matthew D. Hoffman; Dave Moore; Christopher Suter; Srinivas Vasudevan; Alexey Radul; Matthew J. Johnson 002; Rif A. Saurous (CoRR, 2018)

Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language. - Matthew D. Hoffman; Matthew J. Johnson 002; Dustin Tran (CoRR, 2018)

The LORACs Prior for VAEs: Letting the Trees Speak for the Data. - Sharad Vikram; Matthew D. Hoffman; Matthew J. Johnson 002 (AISTATS, 2019)

SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning. - Marvin Zhang; Sharad Vikram; Laura M. Smith; Pieter Abbeel; Matthew J. Johnson 002; Sergey Levine (ICML, 2019)

Learning Differential Equations that are Easy to Solve. - Jacob Kelly; Jesse Bettencourt; Matthew J. Johnson 002; David Duvenaud (NeurIPS, 2020)

Parallelism-preserving automatic differentiation for second-order array languages. - Adam Paszke; Matthew J. Johnson 002; Roy Frostig; Dougal Maclaurin (FHPNC@ICFP, 2021)

Decomposing reverse-mode automatic differentiation. - Roy Frostig; Matthew J. Johnson 002; Dougal Maclaurin; Adam Paszke; Alexey Radul (CoRR, 2021)

Scaling Language Models: Methods, Analysis & Insights from Training Gopher. - Jack W. Rae; Sebastian Borgeaud; Trevor Cai; Katie Millican; Jordan Hoffmann; H. Francis Song; John Aslanides; Sarah Henderson; Roman Ring; Susannah Young; Eliza Rutherford; Tom Hennigan; et al. (CoRR, 2021)

Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming. - Adam Paszke; Daniel D. Johnson 001; David Duvenaud; Dimitrios Vytiniotis; Alexey Radul; Matthew J. Johnson 002; Jonathan Ragan-Kelley; Dougal Maclaurin (Proc. ACM Program. Lang., 2021)

Unified Scaling Laws for Routed Language Models. - Aidan Clark; Diego de Las Casas; Aurelia Guy; Arthur Mensch; Michela Paganini; Jordan Hoffmann; Bogdan Damoc; Blake A. Hechtman; Trevor Cai; Sebastian Borgeaud; George van den Driessche 002; Eliza Rutherford; et al. (ICML, 2022)

Unified Scaling Laws for Routed Language Models. - Aidan Clark; Diego de Las Casas; Aurelia Guy; Arthur Mensch; Michela Paganini; Jordan Hoffmann; Bogdan Damoc; Blake A. Hechtman; Trevor Cai; Sebastian Borgeaud; George van den Driessche 002; Eliza Rutherford; et al. (CoRR, 2022)

You Only Linearize Once: Tangents Transpose to Gradients. - Alexey Radul; Adam Paszke; Roy Frostig; Matthew J. Johnson 002; Dougal Maclaurin (Proc. ACM Program. Lang., 2023)