Shubhendu Trivedi
- Artificial Intelligence
- Computer Science Applications top 10%
- Materials Chemistry
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Co-authors
- Risi KondorZachary A. PardosBrandon AndersonGábor N. SárközyNeil T. HeffernanDavid McAllesterGreg ShakhnarovichZhen Lin
- Topics
- Online Learning and Analytics (4 papers)Machine Learning and Algorithms (4 papers)Intelligent Tutoring Systems and Adaptive Learning (4 papers)
- Journals
- The Journal of Chemical PhysicsNeural Information Processing SystemsInternational Conference on Machine Learning
- Partner nations
- United StatesJapan
In The Last Decade
Shubhendu Trivedi
9 papers receiving 101 citations
Peers
Comparison fields: 5 of 51
- Artificial Intelligence 46
- Computer Science Applications 25
- Materials Chemistry 25
- Computer Vision and Pattern Recognition 24
- Computational Theory and Mathematics 19
Countries citing papers authored by Shubhendu Trivedi
This map shows the geographic impact of Shubhendu Trivedi'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 Shubhendu Trivedi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shubhendu Trivedi more than expected).
Fields of papers citing papers by Shubhendu Trivedi
This network shows the impact of papers produced by Shubhendu Trivedi. 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 Shubhendu Trivedi. The network helps show where Shubhendu Trivedi may publish in the future.
Co-authorship network of co-authors of Shubhendu Trivedi
This figure shows the co-authorship network connecting the top 25 collaborators of Shubhendu Trivedi. A scholar is included among the top collaborators of Shubhendu Trivedi 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 Shubhendu Trivedi. Shubhendu Trivedi 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 | Covariant Compositional Networks For Learning Graphs. | 2 |
| 4 | On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups | 23 |
| 5 | 34 | |
| 6 | Discriminative Metric Learning by Neighborhood Gerrymandering | 4 |
| 7 | A consistent estimator of the expected gradient outerproduct | 1 |
| 8 | Applying Clustering to the Problem of Predicting Retention within an ITS: Comparing Regularity Clustering with Traditional Methods. | 1 |
| 9 | Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction. | 6 |
| 10 | The Real World Significance of Performance Prediction. | 11 |
| 11 | Spectral Clustering in Educational Data Mining. | 22 |
About Shubhendu Trivedi
Shubhendu Trivedi is a scholar working on Computer Science Applications, Artificial Intelligence and Computational Theory and Mathematics, having authored 11 papers that have together received 106 indexed citations. Recurring topics across this work include Online Learning and Analytics (4 papers), Machine Learning and Algorithms (4 papers) and Intelligent Tutoring Systems and Adaptive Learning (4 papers). The work is most often cited by research in Computer Science Applications (25 citations), Artificial Intelligence (46 citations) and Computational Theory and Mathematics (19 citations). Shubhendu Trivedi has collaborated with scholars based in United States and Japan. Frequent co-authors include Risi Kondor, Zachary A. Pardos, Brandon Anderson, Gábor N. Sárközy, Neil T. Heffernan, David McAllester, Greg Shakhnarovich, Zhen Lin, Veena R. Agarwal and Gregory Shakhnarovich. Their work appears in journals such as The Journal of Chemical Physics, Neural Information Processing Systems and International Conference on Machine Learning.
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.