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.
Information-theoretic metric learning
20071.4k citationsPrateek Jain, Suvrit Sra et al.profile →
Co-clustering documents and words using bipartite spectral graph partitioning
Citations per year, relative to Inderjit S. Dhillon Inderjit S. Dhillon (= 1×)
peers
Alexander J. Smola
Countries citing papers authored by Inderjit S. Dhillon
Since
Specialization
Citations
This map shows the geographic impact of Inderjit S. Dhillon'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 Inderjit S. Dhillon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Inderjit S. Dhillon more than expected).
Fields of papers citing papers by Inderjit S. Dhillon
This network shows the impact of papers produced by Inderjit S. Dhillon. 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 Inderjit S. Dhillon. The network helps show where Inderjit S. Dhillon may publish in the future.
Co-authorship network of co-authors of Inderjit S. Dhillon
This figure shows the co-authorship network connecting the top 25 collaborators of Inderjit S. Dhillon.
A scholar is included among the top collaborators of Inderjit S. Dhillon 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 Inderjit S. Dhillon. Inderjit S. Dhillon is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Xiong, Yuanhao, Wei-Cheng Chang, Cho‐Jui Hsieh, Hsiang‐Fu Yu, & Inderjit S. Dhillon. (2022). Extreme Zero-Shot Learning for Extreme Text Classification. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 5455–5468.7 indexed citations
3.
Chen, Patrick, Hsiang‐Fu Yu, Inderjit S. Dhillon, & Cho‐Jui Hsieh. (2021). DRONE: Data-aware Low-rank Compression for Large NLP Models. Neural Information Processing Systems. 34.14 indexed citations
4.
Zhang, Huan, Hongge Chen, Zhao Song, et al.. (2019). The Limitations of Adversarial Training and the Blind-Spot Attack. arXiv (Cornell University).11 indexed citations
Lei, Qi, Jinfeng Yi, Roman Vaculín, Lingfei Wu, & Inderjit S. Dhillon. (2017). Similarity Preserving Representation Learning for Time Series Analysis.. arXiv (Cornell University).8 indexed citations
7.
Yu, Hsiang‐Fu, Nikhil Rao, & Inderjit S. Dhillon. (2016). Temporal regularized matrix factorization for high-dimensional time series prediction. Neural Information Processing Systems. 29. 847–855.215 indexed citations
8.
Yen, Ian En-Hsu, Xiangru Huang, Kai Zhong, Pradeep Ravikumar, & Inderjit S. Dhillon. (2016). PD-sparse: a primal and dual sparse approach to extreme multiclass and multilabel classification. International Conference on Machine Learning. 3069–3077.65 indexed citations
9.
Natarajan, Nagarajan, Oluwasanmi Koyejo, Pradeep Ravikumar, & Inderjit S. Dhillon. (2016). Optimal classification with multivariate losses. International Conference on Machine Learning. 1530–1538.2 indexed citations
10.
Zhong, Kai, Prateek Jain, & Inderjit S. Dhillon. (2016). Mixed Linear Regression with Multiple Components. Neural Information Processing Systems. 29. 2190–2198.7 indexed citations
11.
Si, Si, Cho‐Jui Hsieh, & Inderjit S. Dhillon. (2016). Computationally efficient Nyström approximation using fast transforms. International Conference on Machine Learning. 2655–2663.4 indexed citations
12.
Zhong, Kai, et al.. (2015). A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models. International Conference on Machine Learning. 2418–2426.3 indexed citations
13.
Yen, Ian En-Hsu, Kai Zhong, Cho‐Jui Hsieh, Pradeep Ravikumar, & Inderjit S. Dhillon. (2015). Sparse Linear Programming via primal and dual augmented coordinate descent. Neural Information Processing Systems. 28. 2368–2376.10 indexed citations
Jain, Prateek, Raghu Meka, & Inderjit S. Dhillon. (2010). Guaranteed Rank Minimization via Singular Value Projection. arXiv (Cornell University). 23. 937–945.240 indexed citations
18.
Sra, Suvrit, Joel A. Tropp, & Inderjit S. Dhillon. (2004). Triangle Fixing Algorithms for the Metric Nearness Problem. Neural Information Processing Systems. 17. 361–368.4 indexed citations
Dhillon, Inderjit S., George I. Fann, & Beresford Ν. Parlett. (1997). Application of a New Algorithm for the Symmetric Eigenproblem to Computational Quantum Chemistry.. PPSC.12 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.