Kush Bhatia
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Information Systems
- Control and Systems Engineering
- Computational Theory and Mathematics
- Co-authors
- Purushottam KarPrateek JainManik VarmaHimanshu JainAnca D. DraganSandy H. HuangPieter AbbeelPeter L. Bartlett
- Topics
- Sparse and Compressive Sensing Techniques (3 papers)Machine Learning and Algorithms (3 papers)Statistical Methods and Inference (3 papers)
- Journals
- SHILAP Revista de lepidopterologíaJournal of the American Statistical AssociationComputer Vision and Image Understanding
- Partner nations
- United StatesIndiaSwitzerland
In The Last Decade
Kush Bhatia
10 papers receiving 329 citations
Peers
Comparison fields: 5 of 54
- Artificial Intelligence 279
- Computer Vision and Pattern Recognition 78
- Information Systems 44
- Control and Systems Engineering 41
- Computational Theory and Mathematics 33
Countries citing papers authored by Kush Bhatia
This map shows the geographic impact of Kush Bhatia'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 Kush Bhatia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kush Bhatia more than expected).
Fields of papers citing papers by Kush Bhatia
This network shows the impact of papers produced by Kush Bhatia. 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 Kush Bhatia. The network helps show where Kush Bhatia may publish in the future.
Co-authorship network of co-authors of Kush Bhatia
This figure shows the co-authorship network connecting the top 25 collaborators of Kush Bhatia. A scholar is included among the top collaborators of Kush Bhatia 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 Kush Bhatia. Kush Bhatia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 3 | |
| 3 | 0 | |
| 4 | Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression | 1 |
| 5 | Politex: Regret Bounds for Policy Iteration using Expert Prediction | 11 |
| 6 | Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation | 1 |
| 7 | 51 | |
| 8 | 50 | |
| 9 | Consistent Robust Regression | 15 |
| 10 | 5 | |
| 11 | Sparse local embeddings for extreme multi-label classification | 176 |
| 12 | 28 |
About Kush Bhatia
Kush Bhatia is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research, having authored 12 papers that have together received 341 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (3 papers), Machine Learning and Algorithms (3 papers) and Statistical Methods and Inference (3 papers). The work is most often cited by research in Artificial Intelligence (279 citations), Computer Vision and Pattern Recognition (78 citations) and Computational Mathematics (2 citations). Kush Bhatia has collaborated with scholars based in United States, India and Switzerland. Frequent co-authors include Purushottam Kar, Prateek Jain, Manik Varma, Himanshu Jain, Anca D. Dragan, Sandy H. Huang, Pieter Abbeel, Peter L. Bartlett, Prateek Jain and Ashwin Pananjady. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and Computer Vision and Image Understanding.
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.