Avinava Dubey

1.1k citations
21 papers · 211 · h-index 8

Impact in

Papers in

Avinava Dubey

19 papers receiving 203 citations

Peers

Avinava Dubey
Comparison fields: 5 of 48
  • Software 23
  • Artificial Intelligence 135
  • Information Systems 94
  • Health Informatics 5
  • Signal Processing 25
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Junmei Wang China
Ahmed Salem China
Limin Liu China
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Patrick K. Nicholson United States
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Citations per year

Countries citing papers authored by Avinava Dubey

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

The 25 scholars most cited alongside Avinava Dubey, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Avinava Dubey Line = papers co-authored together Avinava Dubey links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201282
2 201330
3
Contextual Explanation Networks
202016
4
Bayesian nonparametric kernel-learning
201612
5 201912
6 201412
7
Estimating accuracy from unlabeled data: a Bayesian approach
20169
8 20119
9 20187
10 20245
11 20094
12
Large-scale Distributed Dependent Nonparametric Trees
20153
13 20243
14 20112
15 20231
16 20241
17 20251
18
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
20201
19 20181
20 20240

About Avinava Dubey

Avinava Dubey is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Information Systems and Management Science and Operations Research, having authored 21 papers that have together received 211 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (5 papers), Natural Language Processing Techniques (3 papers), Machine Learning and Data Classification (3 papers), Topic Modeling (3 papers), Genomics and Chromatin Dynamics (2 papers), Statistical Methods and Inference (2 papers), Machine Learning and Algorithms (2 papers) and Gaussian Processes and Bayesian Inference (2 papers). The work is most often cited by research in Software (23 citations), Artificial Intelligence (135 citations), Information Systems (94 citations), Health Informatics (5 citations) and Signal Processing (25 citations). Avinava Dubey has collaborated with scholars based in United States, India and Singapore. Frequent co-authors include Eric P. Xing, Senthil Mani, Vibha Singhal Sinha, 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 Cell Genomics, Computational Linguistics, Journal of Machine Learning Research, Blood and Cancer Genetics.

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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