Matthew D. Hoffman
- Statistics and Probability top 0.5%
- Markov Chains and Monte Carlo Methods 6
- Artificial Intelligence top 0.2%
- Gaussian Processes and Bayesian Inference 12
- Bayesian Methods and Mixture Models 8
- Machine Learning and Algorithms 5
- General Decision Sciences top 5%
- Computational Mathematics top 5%
- Signal Processing top 1%
- Music and Audio Processing 18
- Speech and Audio Processing 13
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- Music Technology and Sound Studies 7
- Generative Adversarial Networks and Image Synthesis 5
- Co-authors
- David M. BleiMarcus A. BrubakerJiqiang GuoDaniel C. LeeMichael BetancourtPeter LiAllen RiddellBen Goodrich
- Journals
- Journal of Machine Learning Research (2 papers)Nature Communications (1 paper)IEEE Signal Processing Magazine (1 paper)
- Partner nations
- United StatesCanadaPoland
In The Last Decade
Matthew D. Hoffman
52 papers receiving 7.7k citations
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-authorship network
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
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 1 | |
| 2 | 2024 | 8 | |
| 3 | What Are Bayesian Neural Network Posteriors Really Like | 2021 | 3 |
| 4 | Improving the Gating Mechanism of Recurrent Neural Networks | 2020 | 5 |
| 5 | RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning. | 2020 | 2 |
| 6 | Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics | 2020 | 1 |
| 7 | 2017 | 157 | |
| 8 | Stan: A Probabilistic Programming Languagebreakdown → | 2017 | 496 |
| 9 | Deep Probabilistic Programming | 2017 | 9 |
| 10 | Stochastic Structured Variational Inference | 2014 | 11 |
| 11 | On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning | 2014 | 49 |
| 12 | A Generative Product-of-Filters Model of Audio | 2014 | 2 |
| 13 | Hedging Strategies for Bayesian Optimization | 2010 | 1 |
| 14 | Bayesian Nonparametric Matrix Factorization for Recorded Music | 2010 | 88 |
| 15 | DATA-DRIVEN RECOMPOSITION USING THE HIERARCHICAL DIRICHLET PROCESS HIDDEN MARKOV MODEL | 2009 | 5 |
| 16 | An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward | 2009 | 12 |
| 17 | Bayesian Spectral Matching: Turning Young MC into MC Hammer via MCMC Sampling | 2009 | 2 |
| 18 | THE FEATSYNTH FRAMEWORK FOR FEATURE-BASED SYNTHESIS: DESIGN AND APPLICATIONS | 2007 | 4 |
| 19 | Bayesian Policy Learning with Trans-Dimensional MCMC | 2007 | 15 |
| 20 | Feature-Based Synthesis: Mapping Acoustic and Perceptual Features onto Synthesis Parameters | 2006 | 15 |
About Matthew D. Hoffman
Matthew D. Hoffman is a scholar working on Signal Processing, Artificial Intelligence and Statistics and Probability, 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), Generative Adversarial Networks and Image Synthesis (5 papers) and Machine Learning and Algorithms (5 papers). The work is most often cited by research in Statistics and Probability (873 citations), Artificial Intelligence (2.6k citations) and General Decision Sciences (117 citations). Matthew D. Hoffman has collaborated with scholars based in United States, Canada and Poland. Frequent co-authors include David M. Blei, Marcus A. Brubaker, Jiqiang Guo, Daniel C. Lee, Michael Betancourt, Peter Li, Allen Riddell, Ben Goodrich, Bob Carpenter and Andrew Gelman. Their work appears in journals such as Journal of Machine Learning Research, Nature Communications, IEEE Signal Processing Magazine, IEEE Transactions on Visualization and Computer Graphics 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.