Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Semi-Supervised Hashing for Large-Scale Search
2012597 citationsSanjiv Kumar, Shih‐Fu Chang et al.profile →
Semi-supervised hashing for scalable image retrieval
2010446 citationsSanjiv Kumar, Shih‐Fu Chang et al.profile →
Learning to Hash for Indexing Big Data—A Survey
2015347 citationsWei Liu, Sanjiv Kumar et al.profile →
This map shows the geographic impact of Sanjiv Kumar'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 Sanjiv Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sanjiv Kumar more than expected).
This network shows the impact of papers produced by Sanjiv Kumar. 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 Sanjiv Kumar. The network helps show where Sanjiv Kumar may publish in the future.
Co-authorship network of co-authors of Sanjiv Kumar
This figure shows the co-authorship network connecting the top 25 collaborators of Sanjiv Kumar.
A scholar is included among the top collaborators of Sanjiv Kumar 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 Sanjiv Kumar. Sanjiv Kumar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Parveen, Asma & Sanjiv Kumar. (2021). Effect of Treadmill Exercises on Stress, Cognition and Quality of Life in Stage-1 Hypertensive Patient-An Experimental Study. International Journal of Medical Research & Health Sciences. 10(8). 20–26.1 indexed citations
4.
Liu, Yuhan, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, & Michael Riley. (2020). Learning discrete distributions: user vs item-level privacy. arXiv (Cornell University). 33. 20965–20976.
5.
Guo, Ruiqi, et al.. (2020). Accelerating Large-Scale Inference with Anisotropic Vector Quantization. International Conference on Machine Learning. 1. 3887–3896.22 indexed citations
6.
Yu, Felix X., Ankit Singh Rawat, Aditya Krishna Menon, & Sanjiv Kumar. (2020). Federated Learning with Only Positive Labels. International Conference on Machine Learning. 1. 10946–10956.1 indexed citations
7.
Geng, Quan, Wei Ding, Ruiqi Guo, & Sanjiv Kumar. (2020). Tight Analysis of Privacy and Utility Tradeoff in Approximate Differential Privacy.. International Conference on Artificial Intelligence and Statistics. 89–99.11 indexed citations
8.
Zhang, Jingzhao, Sai Praneeth Karimireddy, Andreas Veit, et al.. (2020). Why are Adaptive Methods Good for Attention Models. Neural Information Processing Systems. 33. 15383–15393.2 indexed citations
9.
Guo, Chuan, Xiang Wu, Daniel Holtmann-Rice, et al.. (2019). Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. Neural Information Processing Systems. 32. 4943–4953.15 indexed citations
10.
Guo, Ruiqi, et al.. (2019). New Loss Functions for Fast Maximum Inner Product Search. arXiv (Cornell University).1 indexed citations
Yen, Ian En-Hsu, Satyen Kale, Felix X. Yu, et al.. (2018). Loss Decomposition for Fast Learning in Large Output Spaces.. International Conference on Machine Learning. 5626–5635.3 indexed citations
Talwalkar, Ameet, Sanjiv Kumar, Mehryar Mohri, & Henry A. Rowley. (2013). Large-scale SVD and manifold learning. Journal of Machine Learning Research. 14(1). 3129–3152.39 indexed citations
17.
Yu, Felix, et al.. (2013). $\propto$SVM for Learning with Label Proportions. International Conference on Machine Learning. 504–512.6 indexed citations
18.
Kumar, Sanjiv, Mehryar Mohri, & Ameet Talwalkar. (2012). Sampling methods for the Nyström method. Journal of Machine Learning Research. 13(1). 981–1006.146 indexed citations
19.
Kumar, Sanjiv, et al.. (2011). Comparative Study between Effectiveness of Dance MovementTherapy and Progressive Relaxation Therapy with Music forStress Management in College Students. Indian Journal of Physiotherapy and Occupational Therapy - An International Journal. 5(2). 172–175.3 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.