Rahul Kidambi
- Artificial Intelligence
- Computational Mechanics
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
- Computational Theory and Mathematics
- Co-authors
- Praneeth NetrapalliSham M. KakadeAaron SidfordPrateek JainRong GeJonathan ChangKilian Q. WeinbergerChuan Guo
- Topics
- Machine Learning and Algorithms (5 papers)Stochastic Gradient Optimization Techniques (4 papers)Sparse and Compressive Sensing Techniques (3 papers)
- Journals
- arXiv (Cornell University)Neural Information Processing SystemsProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Partner nations
- United StatesIsrael
In The Last Decade
Rahul Kidambi
11 papers receiving 37 citations
Peers
Comparison fields: 5 of 27
- Artificial Intelligence 31
- Computational Mechanics 13
- Computer Vision and Pattern Recognition 8
- Computer Networks and Communications 4
- Computational Theory and Mathematics 4
Countries citing papers authored by Rahul Kidambi
This map shows the geographic impact of Rahul Kidambi'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 Rahul Kidambi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rahul Kidambi more than expected).
Fields of papers citing papers by Rahul Kidambi
This network shows the impact of papers produced by Rahul Kidambi. 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 Rahul Kidambi. The network helps show where Rahul Kidambi may publish in the future.
Co-authorship network of co-authors of Rahul Kidambi
This figure shows the co-authorship network connecting the top 25 collaborators of Rahul Kidambi. A scholar is included among the top collaborators of Rahul Kidambi 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 Rahul Kidambi. Rahul Kidambi 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 | Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage | 3 |
| 3 | Making Paper Reviewing Robust to Bid Manipulation Attacks | 4 |
| 4 | MOReL: Model-Based Offline Reinforcement Learning | 2 |
| 5 | The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure. | 6 |
| 6 | Accelerating Stochastic Gradient Descent for Least Squares Regression | 5 |
| 7 | Rethinking learning rate schedules for stochastic optimization | 1 |
| 8 | Accelerating Stochastic Gradient Descent | 10 |
| 9 | Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. | 4 |
| 10 | Submodular hamming metrics | 1 |
| 11 | 2 | |
| 12 | 2 |
About Rahul Kidambi
Rahul Kidambi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 12 papers that have together received 40 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (5 papers), Stochastic Gradient Optimization Techniques (4 papers) and Sparse and Compressive Sensing Techniques (3 papers). The work is most often cited by research in Artificial Intelligence (31 citations), Structural Biology (1 citation) and Computational Mechanics (13 citations). Rahul Kidambi has collaborated with scholars based in United States and Israel. Frequent co-authors include Praneeth Netrapalli, Sham M. Kakade, Aaron Sidford, Prateek Jain, Rong Ge, Jonathan Chang, Kilian Q. Weinberger, Chuan Guo, Sreeram Kannan and Aravind Rajeswaran. Their work appears in journals such as arXiv (Cornell University), Neural Information Processing Systems and Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.
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.