Shipra Agrawal
- Management Science and Operations Research top 1%
- Computer Networks and Communications top 5%
- Artificial Intelligence top 5%
- Electrical and Electronic Engineering
- Management Information Systems top 5%
- Co-authors
- Navin GoyalYinyu YeZizhuo WangNikhil R. DevanurYunhao TangIsrar-ul H. AnsariHemlata DurgapalSubrat Kumar Panda
- Topics
- Advanced Bandit Algorithms Research (18 papers)Optimization and Search Problems (10 papers)Reinforcement Learning in Robotics (7 papers)
- Partner nations
- United StatesIndiaCanada
In The Last Decade
Shipra Agrawal
34 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 98
- Management Science and Operations Research 536
- Computer Networks and Communications 359
- Artificial Intelligence 323
- Electrical and Electronic Engineering 127
- Management Information Systems 112
Countries citing papers authored by Shipra Agrawal
This map shows the geographic impact of Shipra Agrawal'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 Shipra Agrawal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shipra Agrawal more than expected).
Fields of papers citing papers by Shipra Agrawal
This network shows the impact of papers produced by Shipra Agrawal. 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 Shipra Agrawal. The network helps show where Shipra Agrawal may publish in the future.
Co-authorship network of co-authors of Shipra Agrawal
This figure shows the co-authorship network connecting the top 25 collaborators of Shipra Agrawal. A scholar is included among the top collaborators of Shipra Agrawal 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 Shipra Agrawal. Shipra Agrawal 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 | 5 | |
| 3 | 9 | |
| 4 | 8 | |
| 5 | Reinforcement Learning for Integer Programming: Learning to Cut | 11 |
| 6 | 45 | |
| 7 | 21 | |
| 8 | 16 | |
| 9 | Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy. | 9 |
| 10 | Bandits with Delayed Anonymous Feedback. | 0 |
| 11 | 16 | |
| 12 | Linear Contextual Bandits with Knapsacks | 18 |
| 13 | An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives | 12 |
| 14 | 17 | |
| 15 | 6 | |
| 16 | 203 | |
| 17 | 15 | |
| 18 | 93 | |
| 19 | 3 | |
| 20 | 116 |
About Shipra Agrawal
Shipra Agrawal is a scholar working on Management Science and Operations Research, Nephrology and Computer Networks and Communications, having authored 36 papers that have together received 1.1k indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (18 papers), Optimization and Search Problems (10 papers) and Reinforcement Learning in Robotics (7 papers). The work is most often cited by research in Management Science and Operations Research (536 citations), Computer Networks and Communications (359 citations) and Hepatology (112 citations). Shipra Agrawal has collaborated with scholars based in United States, India and Canada. Frequent co-authors include Navin Goyal, Yinyu Ye, Zizhuo Wang, Nikhil R. Devanur, Yunhao Tang, Israr-ul H. Ansari, Hemlata Durgapal, Subrat Kumar Panda, Shahid Jameel and Mark Peters. Their work appears in journals such as Blood, Journal of Virology and Operations Research.
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.