Daniel M. Roy
Impact in
- Software top 5%
- Software Testing and Debugging Techniques
- Hardware and Architecture top 5%
- Parallel Computing and Optimization Techniques
Papers in
-
- Bayesian Methods and Mixture Models 9
- Bayesian Modeling and Causal Inference 9
- Gaussian Processes and Bayesian Inference 7
- Machine Learning and Algorithms 7
- Stochastic Gradient Optimization Techniques 6
- Neural Networks and Applications 5
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- Statistical Methods and Inference 5
- Co-authors
- Martin Rinard (5 shared papers)Joshua B. Tenenbaum (8 shared papers)Vikash K. Mansinghka (4 shared papers)Peter Orbanz (2 shared papers)Cristian Cadar (3 shared papers)Noah D. Goodman (3 shared papers)Yee Whye Teh (3 shared papers)Keith Bonawitz (1 shared paper)
- Journals
- The Annals of Statistics (3 papers)Bernoulli (2 papers)Mathematical Structures in Computer Science (1 paper)Journal of the ACM (1 paper)Bayesian Analysis (1 paper)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Daniel M. Roy
54 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 102
- Software 135
- Hardware and Architecture 197
- Artificial Intelligence 845
- Signal Processing 174
- Statistics and Probability 115
Countries citing papers authored by Daniel M. Roy
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).
Fields of papers citing papers by Daniel M. Roy
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-authors
The 25 scholars most cited alongside Daniel M. Roy, 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 57 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Enhancing server availability and security through failure-oblivious computing | 2004 | 258 |
| 2 | 2012 | 247 | |
| 3 | 2014 | 101 | |
| 4 | 2015 | 100 | |
| 5 | 2005 | 73 | |
| 6 | 2014 | 68 | |
| 7 | 2011 | 67 | |
| 8 | The Mondrian Process | 2008 | 51 |
| 9 | Random function priors for exchangeable arrays with applications to graphs and relational data | 2012 | 47 |
| 10 | 2007 | 41 | |
| 11 | 2011 | 35 | |
| 12 | The Lottery Ticket Hypothesis at Scale | 2019 | 28 |
| 13 | Meeting deadlines in hard real-time systems : the rate monotonic approach | 1999 | 27 |
| 14 | Efficient Bayesian task-level transfer learning | 2007 | 25 |
| 15 | Bayesian Policy Search with Policy Priors | 2012 | 20 |
| 16 | Meeting Deadlines in Hard Real-Time Systems | 1999 | 17 |
| 17 | 2011 | 15 | |
| 18 | 2013 | 12 | |
| 19 | 2019 | 10 | |
| 20 | 2021 | 10 |
About Daniel M. Roy
Daniel M. Roy is a scholar working on Artificial Intelligence, Statistics and Probability, Computational Theory and Mathematics, Mathematical Physics and Computer Vision and Pattern Recognition, having authored 57 papers that have together received 1.4k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (9 papers), Bayesian Modeling and Causal Inference (9 papers), Computability, Logic, AI Algorithms (8 papers), Gaussian Processes and Bayesian Inference (7 papers), Machine Learning and Algorithms (7 papers), Stochastic Gradient Optimization Techniques (6 papers), Statistical Methods and Inference (5 papers) and Neural Networks and Applications (5 papers). The work is most often cited by research in Software (135 citations), Hardware and Architecture (197 citations), Artificial Intelligence (845 citations), Signal Processing (174 citations) and Statistics and Probability (115 citations). Daniel M. Roy has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Martin Rinard, Joshua B. Tenenbaum, Vikash K. Mansinghka, Peter Orbanz, Cristian Cadar, Noah D. Goodman, Yee Whye Teh, Keith Bonawitz, Zoubin Ghahramani and Gintare Karolina Dziugaite. Their work appears in journals such as The Annals of Statistics, Bernoulli, Mathematical Structures in Computer Science, Journal of the ACM and Bayesian Analysis.
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.