Rahul Kidambi

599 total citations
12 papers, 40 citations indexed

About

Rahul Kidambi is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Rahul Kidambi has authored 12 papers receiving a total of 40 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 3 papers in Information Systems and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Rahul Kidambi's work include Machine Learning and Algorithms (5 papers), Stochastic Gradient Optimization Techniques (4 papers) and Sparse and Compressive Sensing Techniques (3 papers). Rahul Kidambi is often cited by papers focused on Machine Learning and Algorithms (5 papers), Stochastic Gradient Optimization Techniques (4 papers) and Sparse and Compressive Sensing Techniques (3 papers). Rahul Kidambi collaborates with scholars based in United States and Israel. Rahul Kidambi's co-authors include Praneeth Netrapalli, Sham M. Kakade, Aaron Sidford, Prateek Jain, Rong Ge, Kilian Q. Weinberger, Chuan Guo, Aravind Rajeswaran, Masatoshi Uehara and W. Sun and has published in prestigious 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.

In The Last Decade

Rahul Kidambi

11 papers receiving 37 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Rahul Kidambi United States	5	31	13	8	4	4	12	40
Michał Dereziński United States	4	24 0.8×	12 0.9×	10 1.3×	3 0.8×	8 2.0×	18	43
Gauthier Gidel Canada	5	28 0.9×	16 1.2×	6 0.8×	5 1.3×	4 1.0×	12	36
Aldo Pacchiano United States	4	53 1.7×	5 0.4×	10 1.3×	3 0.8×	6 1.5×	19	66
Hongzhou Lin United States	4	17 0.5×	9 0.7×	10 1.3×	3 0.8×	5 1.3×	8	42
Wenlin Chen United States	2	26 0.8×	3 0.2×	8 1.0×	8 2.0×	5 1.3×	3	50
Vaishnavh Nagarajan United States	4	26 0.8×	10 0.8×	11 1.4×	5 1.3×	2 0.5×	7	44
Theodor Misiakiewicz United States	5	37 1.2×	16 1.2×	14 1.8×	1 0.3×	4 1.0×	7	60
Behrooz Ghorbani United States	4	47 1.5×	6 0.5×	18 2.3×	2 0.5×	3 0.8×	6	57
Colin Wei United States	4	63 2.0×	8 0.6×	28 3.5×	2 0.5×	2 0.5×	8	72
Alireza Makhzani Canada	4	20 0.6×	12 0.9×	18 2.3×	10 2.5×	2 0.5×	7	41

Countries citing papers authored by Rahul Kidambi

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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12 of 12 papers shown

Kidambi, Rahul, et al.. (2022). Counterfactual Learning To Rank for Utility-Maximizing Query Autocompletion. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 791–802.

Chang, Jonathan, et al.. (2021). Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage. arXiv (Cornell University). 34. 3 indexed citations

Guo, Chuan, et al.. (2021). Making Paper Reviewing Robust to Bid Manipulation Attacks. 139. 11240–11250. 4 indexed citations

Kidambi, Rahul, Aravind Rajeswaran, Praneeth Netrapalli, & Thorsten Joachims. (2020). MOReL: Model-Based Offline Reinforcement Learning. Neural Information Processing Systems. 33. 21810–21823. 2 indexed citations

Ge, Rong, Sham M. Kakade, Rahul Kidambi, & Praneeth Netrapalli. (2019). The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure.. arXiv (Cornell University). 6 indexed citations

Jain, Prateek, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, & Aaron Sidford. (2018). Accelerating Stochastic Gradient Descent for Least Squares Regression. Conference on Learning Theory. 545–604. 5 indexed citations

Ge, Rong, Sham M. Kakade, Rahul Kidambi, & Praneeth Netrapalli. (2018). Rethinking learning rate schedules for stochastic optimization. 1 indexed citations

Jain, Prateek, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, & Aaron Sidford. (2017). Accelerating Stochastic Gradient Descent. arXiv (Cornell University). 10 indexed citations

Jain, Prateek, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, & Aaron Sidford. (2016). Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging.. arXiv (Cornell University). 4 indexed citations

10.

Gillenwater, Jennifer, Rishabh Iyer, Bethany Lusch, Rahul Kidambi, & Jeff Bilmes. (2015). Submodular hamming metrics. arXiv (Cornell University). 28. 3141–3149. 1 indexed citations

11.

Kidambi, Rahul & Sreeram Kannan. (2015). On Shannon capacity and causal estimation. 14. 988–992. 2 indexed citations

12.

Kidambi, Rahul, et al.. (2012). Deformable trellises on factor graphs for robust microtubule tracking in clutter. 676–679. 2 indexed citations

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.

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