Anandkumar, Animashree

Article from CaltechAUTHORS

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• Agarwal, Alekh and Anandkumar, Animashree, el al. (2017) A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries; IEEE Transactions on Information Theory; Vol. 63; No. 1; 575-592; 10.1109/TIT.2016.2614684
• Anandkumar, Animashree and Ge, Rong, el al. (2017) Analyzing Tensor Power Method Dynamics in Overcomplete Regime; Journal of Machine Learning Research; Vol. 18; No. 22; 1-40; 10.48550/arXiv.1411.1488
• Agarwal, Alekh and Anandkumar, Animashree, el al. (2016) Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization; SIAM Journal of Optimization; Vol. 26; No. 4; 2775-2799; 10.1137/140979861
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• Anandkumar, Animashree and Hsu, Daniel, el al. (2015) When Are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity; Journal of Machine Learning Research; Vol. 16; 2643-2694; 10.48550/arXiv.1308.2853
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• Anandkumar, Animashree and Foster, Dean P., el al. (2015) A Spectral Algorithm for Latent Dirichlet Allocation; Algorithmica; Vol. 72; No. 1; 193-214; 10.1007/s00453-014-9909-1
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• Anandkumar, Animashree and Ge, Rong, el al. (2014) Tensor Decompositions for Learning Latent Variable Models; Journal of Machine Learning Research; Vol. 15; 2773-2832; 10.48550/arXiv.1210.7559
• Anandkumar, Animashree and Ge, Rong, el al. (2014) A Tensor Approach to Learning Mixed Membership Community Models; Journal of Machine Learning Research; Vol. 15; 2239-2312; 10.48550/arXiv.1302.2684
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• Janzamin, Majid and Anandkumar, Animashree (2014) High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models; Journal of Machine Learning Research; Vol. 15; 1549-1591; 10.48550/arXiv.1211.0919
• Anandkumar, Animashree and He, Ting, el al. (2013) Seeing through black boxes: Tracking transactions through queues under monitoring resource constraints; Performance Evaluation; Vol. 70; No. 12; 1090-1110; 10.1016/j.peva.2013.08.003
• Anandkumar, Animashree and Hassidim, Avinatan, el al. (2013) Topology discovery of sparse random graphs with few participants; Random Structures & Algorithms; Vol. 43; No. 1; 16-48; 10.1002/rsa.20420
• Anandkumar, Animashree and Valluvan, Ragupathyraj (2013) Learning loopy graphical models with latent variables: Efficient methods and guarantees; Annals of Statistics; Vol. 41; No. 2; 401-435; 10.48550/arXiv.1203.3887
• Anandkumar, Animashree and Tan, Vincent Y. F., el al. (2012) High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion; Journal of Machine Learning Research; Vol. 13; 2293-2337; 10.48550/arXiv.1107.1270
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• Anandkumar, Animashree and Tan, Vincent Y. F., el al. (2012) High-dimensional structure estimation in Ising models: Local separation criterion; Annals of Statistics; Vol. 40; No. 3; 1346-1375; 10.48550/arXiv.1107.1736
• Anandkumar, Amod J. G. and Anandkumar, Animashree, el al. (2011) Robust Rate Maximization Game Under Bounded Channel Uncertainty; IEEE Transactions on Vehicular Technology; Vol. 60; No. 9; 4471-4486; 10.1109/TVT.2011.2171011
• Tan, Vincent Y. F. and Anandkumar, Animashree, el al. (2011) Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates; Journal of Machine Learning Research; Vol. 12; 1617-1653; 10.48550/arXiv.1005.0766
• Choi, Myung Jin and Tan, Vincent Y. F., el al. (2011) Learning Latent Tree Graphical Models; Journal of Machine Learning Research; Vol. 12; 1771-1812; 10.48550/arXiv.1009.2722
• Anandkumar, Animashree and Michael, Nithin, el al. (2011) Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret; IEEE Journal on Selected Areas in Communications; Vol. 29; No. 4; 731-745; 10.1109/JSAC.2011.110406
• Tan, Vincent Y. F. and Anandkumar, Animashree, el al. (2011) A Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov Tree Structures; IEEE Transactions on Information Theory; Vol. 57; No. 3; 1714-1735; 10.1109/TIT.2011.2104513
• Tan, Vincent Y. F. and Anandkumar, Animashree, el al. (2010) Learning Gaussian Tree Models: Analysis of Error Exponents and Extremal Structures; IEEE Transactions on Signal Processing; Vol. 58; No. 5; 2701-2714; 10.1109/TSP.2010.2042478
• Anandkumar, Animashree and Yukich, Joseph E., el al. (2009) Energy scaling laws for distributed inference in random fusion networks; IEEE Journal on Selected Areas in Communications; Vol. 27; No. 7; 1203-1217; 10.1109/JSAC.2009.090916
• Anandkumar, Animashree and Tong, Lang, el al. (2009) Detection of Gauss-Markov Random Fields With Nearest-Neighbor Dependency; IEEE Transactions on Information Theory; Vol. 55; No. 2; 816-827; 10.1109/TIT.2008.2009855
• Anandkumar, Animashree and Tong, Lang, el al. (2008) Optimal Node Density for Detection in Energy-Constrained Random Networks; IEEE Transactions on Signal Processing; Vol. 56; No. 10; 5232-5245; 10.1109/TSP.2008.928514
• Anandkumar, Animashree and Tong, Lang, el al. (2008) Distributed Estimation Via Random Access; IEEE Transactions on Information Theory; Vol. 54; No. 7; 3175-3181; 10.1109/TIT.2008.924652
• Anandkumar, Animashree and Tong, Lang (2007) Type-Based Random Access for Distributed Detection Over Multiaccess Fading Channels; IEEE Transactions on Signal Processing; Vol. 55; No. 10; 5032-5043; 10.1109/TSP.2007.896302