Naresh Manwani
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Information Systems
- Computational Theory and Mathematics
- Industrial and Manufacturing Engineering
- Co-authors
- P. S. SastryAritra GhoshHimanshu KumarLaure Berti‐ÉquilleRuhi Sharma MittalHima PatelShanmukha GuttulaMudit Agarwal
- Topics
- Machine Learning and Algorithms (7 papers)Data Stream Mining Techniques (4 papers)Advanced Bandit Algorithms Research (4 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionIndustrial and Manufacturing Engineering
- Journals
- Information SciencesIEEE Transactions on Neural Networks and Learning SystemsNeurocomputing
- Partner nations
- IndiaUnited StatesFrance
In The Last Decade
Naresh Manwani
13 papers receiving 263 citations
Peers
Comparison fields: 5 of 56
- Artificial Intelligence 213
- Computer Vision and Pattern Recognition 65
- Information Systems 34
- Computational Theory and Mathematics 30
- Industrial and Manufacturing Engineering 26
Countries citing papers authored by Naresh Manwani
This map shows the geographic impact of Naresh Manwani'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 Naresh Manwani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Naresh Manwani more than expected).
Fields of papers citing papers by Naresh Manwani
This network shows the impact of papers produced by Naresh Manwani. 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 Naresh Manwani. The network helps show where Naresh Manwani may publish in the future.
Co-authorship network of co-authors of Naresh Manwani
This figure shows the co-authorship network connecting the top 25 collaborators of Naresh Manwani. A scholar is included among the top collaborators of Naresh Manwani 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 Naresh Manwani. Naresh Manwani 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 | 11 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | Exact Passive-Aggressive Algorithms for Multiclass Classification Using Bandit Feedbacks | 3 |
| 6 | Robust Deep Ordinal Regression under Label Noise | 1 |
| 7 | 8 | |
| 8 | 2 | |
| 9 | 5 | |
| 10 | 130 | |
| 11 | 11 | |
| 12 | 55 | |
| 13 | Learning polyhedral classifiers using logistic function | 10 |
| 14 | 32 |
About Naresh Manwani
Naresh Manwani is a scholar working on Management Science and Operations Research, Artificial Intelligence and Statistics and Probability, having authored 14 papers that have together received 270 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (7 papers), Data Stream Mining Techniques (4 papers) and Advanced Bandit Algorithms Research (4 papers). The work is most often cited by research in Artificial Intelligence (213 citations), Computer Vision and Pattern Recognition (65 citations) and Industrial and Manufacturing Engineering (26 citations). Naresh Manwani has collaborated with scholars based in India, United States and France. Frequent co-authors include P. S. Sastry, Aritra Ghosh, Himanshu Kumar, Laure Berti‐Équille, Ruhi Sharma Mittal, Hima Patel, Shanmukha Guttula, Mudit Agarwal and Amit Kumar Pandey. Their work appears in journals such as Information Sciences, IEEE Transactions on Neural Networks and Learning Systems and Neurocomputing.
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.