Matthew W. Hoffman

2.2k total citations
11 papers, 424 citations indexed

About

Matthew W. Hoffman is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research. According to data from OpenAlex, Matthew W. Hoffman has authored 11 papers receiving a total of 424 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Management Science and Operations Research. Recurrent topics in Matthew W. Hoffman's work include Machine Learning and Algorithms (3 papers), Gaussian Processes and Bayesian Inference (2 papers) and Multimodal Machine Learning Applications (2 papers). Matthew W. Hoffman is often cited by papers focused on Machine Learning and Algorithms (3 papers), Gaussian Processes and Bayesian Inference (2 papers) and Multimodal Machine Learning Applications (2 papers). Matthew W. Hoffman collaborates with scholars based in United States, United Kingdom and Canada. Matthew W. Hoffman's co-authors include José Miguel Hernández-Lobato, Zoubin Ghahramani, Michael A. Gelbart, Ryan P. Adams, Rajesh P. N. Rao, Aaron P. Shon, David B. Grimes, Sergio Gómez Colmenarejo, Misha Denil and Nando de Freitas and has published in prestigious journals such as Neural Networks, Journal of Machine Learning Research and Journal of Hospital Medicine.

In The Last Decade

Matthew W. Hoffman

11 papers receiving 409 citations

Peers

Matthew W. Hoffman
Zhenzhong Wang China
Robert Murphey United States
Ming-Shong Lan United States
Andrea Caponio Italy
Masumi Ishikawa Japan
Thomas Stifter Luxembourg
D.H. Rao Canada
Lukas Brunke Canada
Michael Hofbaur Austria
Junhong Wang China
Zhenzhong Wang China
Matthew W. Hoffman
Citations per year, relative to Matthew W. Hoffman Matthew W. Hoffman (= 1×) peers Zhenzhong Wang

Countries citing papers authored by Matthew W. Hoffman

Since Specialization
Citations

This map shows the geographic impact of Matthew W. 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 W. 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 W. Hoffman more than expected).

Fields of papers citing papers by Matthew W. Hoffman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Matthew W. 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 W. Hoffman. The network helps show where Matthew W. Hoffman may publish in the future.

Co-authorship network of co-authors of Matthew W. Hoffman

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew W. Hoffman. A scholar is included among the top collaborators of Matthew W. 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 W. Hoffman. Matthew W. Hoffman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

11 of 11 papers shown
1.
Barth-Maron, Gabriel, Matthew W. Hoffman, David Budden, et al.. (2018). Distributed Distributional Deterministic Policy Gradients. arXiv (Cornell University). 34 indexed citations
2.
Tran, Dustin, et al.. (2018). Simple, Distributed, and Accelerated Probabilistic Programming. arXiv (Cornell University). 31. 7598–7609. 7 indexed citations
3.
Cabi, Serkan, et al.. (2017). The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously. 207–216. 1 indexed citations
4.
Maheswaranathan, Niru, Matthew W. Hoffman, Sergio Gómez Colmenarejo, et al.. (2017). Learned Optimizers that Scale and Generalize. arXiv (Cornell University). 3751–3760. 38 indexed citations
5.
Hoffman, Matthew W., et al.. (2016). Learning to Learn for Global Optimization of Black Box Functions.. arXiv (Cornell University). 10 indexed citations
6.
Hernández-Lobato, José Miguel, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, & Zoubin Ghahramani. (2016). A General Framework for Constrained Bayesian Optimization using Information-based Search. Journal of Machine Learning Research. 17(1). 5549–5601. 76 indexed citations
7.
Hernández-Lobato, José Miguel, Matthew W. Hoffman, & Zoubin Ghahramani. (2014). Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. arXiv (Cornell University). 27. 918–926. 176 indexed citations
8.
Hoffman, Matthew W.. (2013). Decision making with inference and learning methods. Open Collections. 1 indexed citations
9.
Camins, Bernard, et al.. (2011). The clinical impact of fluoroquinolone resistance in patients with E coli bacteremia. Journal of Hospital Medicine. 6(6). 344–349. 23 indexed citations
10.
Hoffman, Matthew W., et al.. (2009). Cryptic deletion of EGR1 in association with a novel balanced t(5;22)(q31;q11.2) in a patient with myelodysplastic syndrome. Cancer Genetics and Cytogenetics. 191(2). 106–108. 3 indexed citations
11.
Hoffman, Matthew W., David B. Grimes, Aaron P. Shon, & Rajesh P. N. Rao. (2006). A probabilistic model of gaze imitation and shared attention. Neural Networks. 19(3). 299–310. 55 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.

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