Animashree Anandkumar
- Artificial Intelligence top 2%
- Computer Networks and Communications top 5%
- Computational Mathematics top 0.2%
- Computational Mechanics top 5%
- Electrical and Electronic Engineering
- Co-authors
- Rong GeDaniel HsuSham M. KakadeMatus TelgarskyLang TongAlan S. WillskyVincent Y. F. TanAo Tang
- Topics
- Machine Learning and Algorithms (14 papers)Tensor decomposition and applications (14 papers)Distributed Sensor Networks and Detection Algorithms (14 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsIEEE Transactions on Information Theory
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Animashree Anandkumar
93 papers receiving 1.5k citations
Hit Papers
Peers
Comparison fields: 5 of 129
- Artificial Intelligence 654
- Computer Networks and Communications 409
- Computational Mathematics 316
- Computational Mechanics 309
- Electrical and Electronic Engineering 228
Countries citing papers authored by Animashree Anandkumar
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | Robust Reinforcement Learning: A Constrained Game-theoretic Approach | 4 |
| 3 | Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion | 6 |
| 4 | Causal Discovery in Physical Systems from Videos | 2 |
| 5 | Memory Augmented Recursive Neural Networks | 3 |
| 6 | Deep Active Learning for Named Entity Recognition. | 18 |
| 7 | Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations | 16 |
| 8 | Online and differentially-private tensor decomposition | 5 |
| 9 | 16 | |
| 10 | Learning Overcomplete Latent Variable Models through Tensor Methods | 13 |
| 11 | Tensor decompositions for learning latent variable modelsbreakdown → | 371 |
| 12 | 53 | |
| 13 | Learning Sparsely Used Overcomplete Dictionaries | 28 |
| 14 | Provable Learning of Overcomplete Latent Variable Models: Semi-supervised and Unsupervised Settings. | 2 |
| 15 | Provable non-convex robust PCA | 12 |
| 16 | Fast Detection of Overlapping Communities via Online Tensor Methods on GPUs | 6 |
| 17 | Exact Recovery of Sparsely Used Overcomplete Dictionaries. | 19 |
| 18 | Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation | 9 |
| 19 | High-Dimensional Gaussian Graphical Model Selection: Tractable Graph Families | 5 |
| 20 | High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions | 8 |
About Animashree Anandkumar
Animashree Anandkumar is a scholar working on Computational Mathematics, Health Informatics and Artificial Intelligence, having authored 99 papers that have together received 1.6k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (14 papers), Tensor decomposition and applications (14 papers) and Distributed Sensor Networks and Detection Algorithms (14 papers). The work is most often cited by research in Computational Mathematics (316 citations), Health Informatics (68 citations) and Artificial Intelligence (654 citations). Animashree Anandkumar has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Rong Ge, Daniel Hsu, Sham M. Kakade, Matus Telgarsky, Lang Tong, Alan S. Willsky, Vincent Y. F. Tan, Ao Tang, Praneeth Netrapalli and Andrew J. Hung. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Transactions on Information Theory.
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.