This map shows the geographic impact of Daniel M. Roy'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 Daniel M. Roy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel M. Roy more than expected).
This network shows the impact of papers produced by Daniel M. Roy. 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 Daniel M. Roy. The network helps show where Daniel M. Roy may publish in the future.
Co-authorship network of co-authors of Daniel M. Roy
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel M. Roy.
A scholar is included among the top collaborators of Daniel M. Roy 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 Daniel M. Roy. Daniel M. Roy is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Dziugaite, Gintare Karolina, et al.. (2021). Towards a Unified Information-Theoretic Framework for Generalization. arXiv (Cornell University). 34.1 indexed citations
3.
Dziugaite, Gintare Karolina, et al.. (2021). On the role of data in PAC-Bayes. International Conference on Artificial Intelligence and Statistics. 604–612.2 indexed citations
4.
Faghri, Fartash, et al.. (2020). Adaptive Gradient Quantization for Data-Parallel SGD. Neural Information Processing Systems. 33. 3174–3185.3 indexed citations
5.
Fort, Stanislav, et al.. (2020). Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. Neural Information Processing Systems. 33. 5850–5861.1 indexed citations
6.
Khisti, Ashish, et al.. (2020). Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms. Neural Information Processing Systems. 33. 9925–9935.2 indexed citations
7.
Sun, Shengyang, et al.. (2019). Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes. Neural Information Processing Systems. 32. 10802–10812.2 indexed citations
8.
Faghri, Fartash, et al.. (2019). NUQSGD: Improved Communication Efficiency for Data-parallel SGD via Nonuniform Quantization.. arXiv (Cornell University).6 indexed citations
9.
Staton, Sam, et al.. (2018). The Beta-Bernoulli process and algebraic effects. Oxford University Research Archive (ORA) (University of Oxford).1 indexed citations
10.
Grosse, Roger, et al.. (2016). Measuring the reliability of MCMC inference with bidirectional Monte Carlo. Neural Information Processing Systems. 29. 2451–2459.4 indexed citations
11.
Lloyd, James Robert, Peter Orbanz, Zoubin Ghahramani, & Daniel M. Roy. (2012). Random function priors for exchangeable arrays with applications to graphs and relational data. 25. 998–1006.44 indexed citations
12.
Freer, Cameron E. & Daniel M. Roy. (2011). Computable de Finetti measures. Annals of Pure and Applied Logic. 163(5). 530–546.12 indexed citations
13.
Mansinghka, Vikash K., Daniel M. Roy, Eric Jonas, & Joshua B. Tenenbaum. (2009). Exact and approximate sampling by systematic stochastic search. International Conference on Artificial Intelligence and Statistics. 400–407.3 indexed citations
14.
Roy, Daniel M. & Yee Whye Teh. (2008). The Mondrian Process. UCL Discovery (University College London). 21. 1377–1384.49 indexed citations
15.
Roy, Daniel M., et al.. (2007). Discovering Syntactic Hierarchies. eScholarship (California Digital Library). 29(29).
16.
Roy, Daniel M. & Leslie Pack Kaelbling. (2007). Efficient Bayesian task-level transfer learning. International Joint Conference on Artificial Intelligence. 2599–2604.23 indexed citations
17.
Mansinghka, Vikash K., Daniel M. Roy, Ryan Rifkin, & Joshua B. Tenenbaum. (2007). AClass: A simple, online, parallelizable algorithm for probabilistic classification. International Conference on Artificial Intelligence and Statistics. 315–322.4 indexed citations
18.
Rinard, Martin, et al.. (2004). Enhancing server availability and security through failure-oblivious computing. Operating Systems Design and Implementation. 21–21.233 indexed citations
19.
Roy, Daniel M., et al.. (1999). Meeting deadlines in hard real-time systems : the rate monotonic approach.22 indexed citations
20.
Roy, Daniel M.. (1994). The Personal Software Process: Downscaling the factory. NASA Technical Reports Server (NASA).1 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.