Matthew D. Hoffman
Impact in
- Statistics and Probability top 0.5%
- Statistical Methods and Bayesian Inference
- Statistical Methods and Inference
- Artificial Intelligence top 0.2%
- Topic Modeling
- Gaussian Processes and Bayesian Inference
- Bayesian Methods and Mixture Models
- Advanced Graph Neural Networks
Papers in
-
- Gaussian Processes and Bayesian Inference 12
- Bayesian Methods and Mixture Models 8
- Machine Learning and Algorithms 5
-
- Music and Audio Processing 18
- Speech and Audio Processing 13
- Co-authors
- David M. Blei (10 shared papers)Daniel C. Lee (2 shared papers)Peter Li (2 shared papers)Jiqiang Guo (2 shared papers)Michael Betancourt (2 shared papers)Marcus A. Brubaker (2 shared papers)Bob Carpenter (2 shared papers)Allen Riddell (2 shared papers)
- Journals
- Journal of Machine Learning Research (2 papers)IEEE Transactions on Visualization and Computer Graphics (1 paper)Nature Communications (1 paper)IEEE Signal Processing Magazine (1 paper)Journal of Statistical Software (1 paper)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Matthew D. Hoffman
52 papers receiving 7.7k citations
Matthew D. Hoffman's Hit Papers
Peers
Comparison fields: 5 of 223
- Statistics and Probability 873
- Artificial Intelligence 2.6k
- General Decision Sciences 117
- Computational Mathematics 34
- Signal Processing 586
Countries citing papers authored by Matthew D. Hoffman
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-authors
The 25 scholars most cited alongside Matthew D. Hoffman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Stan: A Probabilistic Programming Language Hit paper breakdown → | 2017 | 4257 |
| 2 | Online Learning for Latent Dirichlet Allocation Hit paper breakdown → | 2010 | 814 |
| 3 | Variational Autoencoders for Collaborative Filtering Hit paper breakdown → | 2018 | 739 |
| 4 | Stochastic variational inference Hit paper breakdown → | 2013 | 669 |
| 5 | Stan: A Probabilistic Programming Language Hit paper breakdown → | 2017 | 496 |
| 6 | 2017 | 157 | |
| 7 | 2014 | 99 | |
| 8 | Bayesian Nonparametric Matrix Factorization for Recorded Music | 2010 | 88 |
| 9 | 2016 | 85 | |
| 10 | Portfolio allocation for Bayesian optimization | 2011 | 61 |
| 11 | 2009 | 59 | |
| 12 | On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning | 2014 | 49 |
| 13 | 2008 | 42 | |
| 14 | 2017 | 39 | |
| 15 | 2015 | 25 | |
| 16 | An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation | 2018 | 21 |
| 17 | 2006 | 21 | |
| 18 | Bayesian Policy Learning with Trans-Dimensional MCMC | 2007 | 15 |
| 19 | Feature-Based Synthesis: Mapping Acoustic and Perceptual Features onto Synthesis Parameters | 2006 | 15 |
| 20 | Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo | 2017 | 14 |
About Matthew D. Hoffman
Matthew D. Hoffman is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Statistics and Probability and Information Systems, having authored 54 papers that have together received 7.9k indexed citations. Recurring topics across this work include Music and Audio Processing (18 papers), Speech and Audio Processing (13 papers), Gaussian Processes and Bayesian Inference (12 papers), Bayesian Methods and Mixture Models (8 papers), Music Technology and Sound Studies (7 papers), Markov Chains and Monte Carlo Methods (6 papers), Machine Learning and Algorithms (5 papers) and Generative Adversarial Networks and Image Synthesis (5 papers). The work is most often cited by research in Statistics and Probability (873 citations), Artificial Intelligence (2.6k citations), General Decision Sciences (117 citations), Computational Mathematics (34 citations) and Signal Processing (586 citations). Matthew D. Hoffman has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include David M. Blei, Daniel C. Lee, Peter Li, Jiqiang Guo, Michael Betancourt, Marcus A. Brubaker, Bob Carpenter, Allen Riddell, Ben Goodrich and Andrew Gelman. Their work appears in journals such as Journal of Machine Learning Research, IEEE Transactions on Visualization and Computer Graphics, Nature Communications, IEEE Signal Processing Magazine and Journal of Statistical Software.
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.