Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Stan: A Probabilistic Programming Language
20174.3k citationsBob Carpenter, Matthew D. Hoffman et al.profile →
Countries citing papers authored by Matthew D. Hoffman
Since
Specialization
Citations
This map shows the geographic impact of Matthew D. Hoffman's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Matthew D. Hoffman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew D. Hoffman more than expected).
Fields of papers citing papers by Matthew D. Hoffman
This network shows the impact of papers produced by Matthew D. Hoffman. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Matthew D. Hoffman. The network helps show where Matthew D. Hoffman may publish in the future.
Co-authorship network of co-authors of Matthew D. Hoffman
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew D. Hoffman.
A scholar is included among the top collaborators of Matthew D. Hoffman based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Matthew D. Hoffman. Matthew D. Hoffman is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hoffman, Matthew D., et al.. (2021). An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo. International Conference on Artificial Intelligence and Statistics. 3907–3915.6 indexed citations
5.
Izmailov, Pavel, Sharad Vikram, Matthew D. Hoffman, & Andrew Gordon Wilson. (2021). What Are Bayesian Neural Network Posteriors Really Like. International Conference on Machine Learning. 4629–4640.3 indexed citations
6.
Gu, Albert, et al.. (2020). Improving the Gating Mechanism of Recurrent Neural Networks. International Conference on Machine Learning. 1. 3800–3809.5 indexed citations
7.
Hoffman, Matthew D. & Yi-An Ma. (2020). Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics. International Conference on Machine Learning. 1. 4324–4341.1 indexed citations
8.
Saeedi, Ardavan, Matthew D. Hoffman, Stephen DiVerdi, et al.. (2018). Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. International Conference on Artificial Intelligence and Statistics. 1309–1317.2 indexed citations
9.
Tran, Dustin, Matthew D. Hoffman, Rif A. Saurous, et al.. (2017). Deep Probabilistic Programming. International Conference on Learning Representations.9 indexed citations
Hoffman, Matthew D.. (2017). Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo. International Conference on Machine Learning. 1510–1519.14 indexed citations
12.
Liang, Dawen, Matthew D. Hoffman, & Gautham J. Mysore. (2014). A Generative Product-of-Filters Model of Audio. arXiv (Cornell University).2 indexed citations
Hoffman, Matthew D., David M. Blei, Chong Wang, & John Paisley. (2013). Stochastic variational inference. Journal of Machine Learning Research. 14(1). 1303–1347.669 indexed citations breakdown →
Brochu, Eric, Matthew D. Hoffman, & Nando de Freitas. (2010). Hedging Strategies for Bayesian Optimization. arXiv (Cornell University).1 indexed citations
17.
Hoffman, Matthew D., Nando de Freitas, Randal Douc, & Jan Peters. (2009). An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward. TUbilio (Technical University of Darmstadt). 5. 232–239.12 indexed citations
18.
Hoffman, Matthew D., et al.. (2009). DATA-DRIVEN RECOMPOSITION USING THE HIERARCHICAL DIRICHLET PROCESS HIDDEN MARKOV MODEL. The Journal of the Abraham Lincoln Association. 2008.5 indexed citations
19.
Hoffman, Matthew D. & Perry R. Cook. (2007). THE FEATSYNTH FRAMEWORK FOR FEATURE-BASED SYNTHESIS: DESIGN AND APPLICATIONS. The Journal of the Abraham Lincoln Association. 2007.4 indexed citations
20.
Hoffman, Matthew D. & Perry R. Cook. (2006). Feature-Based Synthesis: Mapping Acoustic and Perceptual Features onto Synthesis Parameters. The Journal of the Abraham Lincoln Association. 2006.15 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.