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.
Tensor decompositions for learning latent variable models
2014371 citationsAnimashree Anandkumar, Rong Ge et al.Journal of Machine Learning Researchprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Animashree Anandkumar
Since
Specialization
Citations
This map shows the geographic impact of Animashree Anandkumar'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 Animashree Anandkumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Animashree Anandkumar more than expected).
Fields of papers citing papers by Animashree Anandkumar
This network shows the impact of papers produced by Animashree Anandkumar. 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 Animashree Anandkumar. The network helps show where Animashree Anandkumar may publish in the future.
Co-authorship network of co-authors of Animashree Anandkumar
This figure shows the co-authorship network connecting the top 25 collaborators of Animashree Anandkumar.
A scholar is included among the top collaborators of Animashree Anandkumar 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 Animashree Anandkumar. Animashree Anandkumar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Anandkumar, Animashree, et al.. (2022). IoT enabled smart bus for COVID‐19. Microwave and Optical Technology Letters. 64(4). 639–642.2 indexed citations
2.
Yu, Jing, et al.. (2021). Robust Reinforcement Learning: A Constrained Game-theoretic Approach. CaltechAUTHORS (California Institute of Technology). 1242–1254.4 indexed citations
3.
Da, Xingye, Zhaoming Xie, David Hoeller, et al.. (2020). Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion. arXiv (Cornell University). 883–894.6 indexed citations
4.
Li, Yunzhu, Antonio Torralba, Animashree Anandkumar, Dieter Fox, & Animesh Garg. (2020). Causal Discovery in Physical Systems from Videos. CaltechAUTHORS (California Institute of Technology). 33. 9180–9192.2 indexed citations
5.
Singh, Sameer, et al.. (2019). Memory Augmented Recursive Neural Networks. CaltechAUTHORS (California Institute of Technology).3 indexed citations
6.
Shen, Yanyao, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, & Animashree Anandkumar. (2018). Deep Active Learning for Named Entity Recognition.. CaltechAUTHORS (California Institute of Technology).18 indexed citations
7.
Anandkumar, Animashree, et al.. (2016). Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations. CaltechAUTHORS (California Institute of Technology). 51. 268–276.16 indexed citations
8.
Wang, Yining & Animashree Anandkumar. (2016). Online and differentially-private tensor decomposition. CaltechAUTHORS (California Institute of Technology). 29. 3539–3547.5 indexed citations
Anandkumar, Animashree, Rong Ge, & Majid Janzamin. (2014). Provable Learning of Overcomplete Latent Variable Models: Semi-supervised and Unsupervised Settings.. arXiv (Cornell University).2 indexed citations
15.
Netrapalli, Praneeth, et al.. (2014). Provable non-convex robust PCA. CaltechAUTHORS (California Institute of Technology). 2. 1107–1115.12 indexed citations
16.
Huang, Furong, et al.. (2013). Fast Detection of Overlapping Communities via Online Tensor Methods on GPUs. arXiv (Cornell University).6 indexed citations
17.
Agarwal, Alekh, Animashree Anandkumar, & Praneeth Netrapalli. (2013). Exact Recovery of Sparsely Used Overcomplete Dictionaries.. arXiv (Cornell University).19 indexed citations
18.
Anandkumar, Animashree, Dean P. Foster, Daniel Hsu, Sham M. Kakade, & Yi-Kai Liu. (2012). Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation. arXiv (Cornell University).9 indexed citations
19.
Anandkumar, Animashree, Vincent Y. F. Tan, & Alan S. Willsky. (2011). High-Dimensional Gaussian Graphical Model Selection: Tractable Graph Families. arXiv (Cornell University).5 indexed citations
20.
Anandkumar, Animashree, Vincent Y. F. Tan, & Alan S. Willsky. (2011). High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions. CaltechAUTHORS (California Institute of Technology). 24. 1863–1871.8 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.