Avinava Dubey
- Artificial Intelligence top 10%
- Information Systems top 10%
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
- Signal Processing
- Co-authors
- Eric P. XingVibha Singhal SinhaSenthil ManiSinead A. WilliamsonAhmed HefnyTom M. MitchellMaruan Al-ShedivatSoumen Chakrabarti
- Topics
- Bayesian Methods and Mixture Models (5 papers)Topic Modeling (3 papers)Machine Learning and Data Classification (3 papers)
- Partner nations
- United StatesIndiaSingapore
In The Last Decade
Avinava Dubey
19 papers receiving 203 citations
Peers
Comparison fields: 5 of 48
- Artificial Intelligence 135
- Information Systems 94
- Computer Vision and Pattern Recognition 30
- Computer Networks and Communications 27
- Signal Processing 25
Countries citing papers authored by Avinava Dubey
This map shows the geographic impact of Avinava Dubey'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 Avinava Dubey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Avinava Dubey more than expected).
Fields of papers citing papers by Avinava Dubey
This network shows the impact of papers produced by Avinava Dubey. 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 Avinava Dubey. The network helps show where Avinava Dubey may publish in the future.
Co-authorship network of co-authors of Avinava Dubey
This figure shows the co-authorship network connecting the top 25 collaborators of Avinava Dubey. A scholar is included among the top collaborators of Avinava Dubey 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 Avinava Dubey. Avinava Dubey is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 5 | |
| 5 | 3 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | Contextual Explanation Networks | 16 |
| 9 | Distributed, partially collapsed MCMC for Bayesian Nonparametrics | 1 |
| 10 | 12 | |
| 11 | 1 | |
| 12 | 7 | |
| 13 | Estimating accuracy from unlabeled data: a Bayesian approach | 9 |
| 14 | Bayesian nonparametric kernel-learning | 12 |
| 15 | Large-scale Distributed Dependent Nonparametric Trees | 3 |
| 16 | 12 | |
| 17 | 30 | |
| 18 | 2 | |
| 19 | 9 | |
| 20 | 4 |
About Avinava Dubey
Avinava Dubey is a scholar working on Artificial Intelligence, Software and Statistics and Probability, having authored 21 papers that have together received 211 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (5 papers), Topic Modeling (3 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Software (23 citations), Artificial Intelligence (135 citations) and Information Systems (94 citations). Avinava Dubey has collaborated with scholars based in United States, India and Singapore. Frequent co-authors include Eric P. Xing, Vibha Singhal Sinha, Senthil Mani, Sinead A. Williamson, Ahmed Hefny, Tom M. Mitchell, Maruan Al-Shedivat, Soumen Chakrabarti, Chiranjib Bhattacharyya and Eduard Hovy. Their work appears in journals such as Blood, Journal of Machine Learning Research and Computational Linguistics.
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.