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Zhao, Eric; Liu, Anqi et al. (2021) Active Learning under Label Shift Proceedings of Machine Learning Research; Vol. 130; 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 13-15 April 2021 , San Diego, CA

Nakka, Yashwanth Kumar; Liu, Anqi et al. (2021) Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems IEEE Robotics and Automation Letters; Vol. 6; No. 2;

Chu, Linda C.; Anandkumar, Animashree et al. (2020) The Potential Dangers of Artificial Intelligence for Radiology and Radiologists Journal of the American College of Radiology; Vol. 17; No. 10;

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Chen, Wuyang; Yu, Zhiding et al. (2020) Automated Synthetic-to-Real Generalization Proceedings of Machine Learning Research; Vol. 119; 37th International Conference on Machine Learning, 13-18 July 2020 , [Online Only]

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Janzamin, Majid; Ge, Rong et al. (2019) Spectral Learning on Matrices and Tensors Foundations and Trends in Machine Learning; Vol. 12; No. 5-6;

Huang, Furong; Naresh, Niranjan Uma et al. (2019) Guaranteed Scalable Learning of Latent Tree Models Proceedings of Machine Learning Research; Vol. 115; 35th Uncertainty in Artificial Intelligence Conference, 22-25 July 2019 , Tel Aviv, Israel

Cvitkovic, Milan; Singh, Badal et al. (2019) Open Vocabulary Learning on Source Code with a Graph-Structured Cache Proceedings of Machine Learning Research; Vol. 97; 36th International Conference on Machine Learning, 9-15 June 2019 , Long Beach, CA

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Kossaifi, Jean; Panagakis, Yannis et al. (2019) TensorLy: Tensor Learning in Python Journal of Machine Learning Research; Vol. 20; No. 26;

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Tschannen, Michael; Khanna, Aran et al. (2018) StrassenNets: Deep Learning with a Multiplication Budget Proceedings of Machine Learning Research; Vol. 80; 35th International Conference on Machine Learning, 10-15 July 2018 , Stockholm, Sweden

Bernstein, Jeremy; Wang, Yu-Xiang et al. (2018) signSGD: Compressed Optimisation for Non-Convex Problems Proceedings of Machine Learning Research; Vol. 80; International Conference on Machine Learning (ICML), 10-15 July 2018 , Stockholm, Sweden

Anandkumar, Anima; Deng, Yuan et al. (2017) Homotopy Analysis for Tensor PCA Proceedings of Machine Learning Research; Vol. 65; Conference on Learning Theory (COLT 2017), 7-10 July 2017 , Amsterdam, Netherlands

Agarwal, Alekh; Anandkumar, Animashree et al. (2017) A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries IEEE Transactions on Information Theory; Vol. 63; No. 1; 2014 Conference on Learning Theory (COLT), June 13-15, 2014 , Barcelona, Spain

Anandkumar, Animashree; Ge, Rong et al. (2017) Analyzing Tensor Power Method Dynamics in Overcomplete Regime Journal of Machine Learning Research; Vol. 18; No. 22;

Agarwal, Alekh; Anandkumar, Animashree et al. (2016) Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization SIAM Journal of Optimization; Vol. 26; No. 4;

Azizzadenesheli, Kamyar; Lazaric, Alessandro et al. (2016) Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies Proceedings of Machine Learning Research; Vol. 49; Conference on Learning Theory (COLT 2016), 23-26 June 2016 , New York, NY

Azizzadenesheli, Kamyar; Lazaric, Alessandro et al. (2016) Reinforcement Learning of POMDPs using Spectral Methods Proceedings of Machine Learning Research; Vol. 49; Conference on Learning Theory (COLT 2016), 23-26 June 2016 , New York, NY

Huang, Furong; Niranjan, U. N. et al. (2015) Online Tensor Methods for Learning Latent Variable Models Journal of Machine Learning Research; Vol. 16;

Anandkumar, Animashree; Hsu, Daniel et al. (2015) When Are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity Journal of Machine Learning Research; Vol. 16;

Anandkumar, Animashree; Foster, Dean P. et al. (2015) A Spectral Algorithm for Latent Dirichlet Allocation Algorithmica; Vol. 72; No. 1;

Sedghi, Hanie; Janzamin, Majid et al. (2014) Provable Tensor Methods for Learning Mixtures of Generalized Linear Models Proceedings of Machine Learning Research; Vol. 51; 19th International Conference on Artificial Intelligence and Statistics, 9-11 May 2016 , Cadiz, Spain

Anandkumar, Animashree; Ge, Rong et al. (2014) Tensor Decompositions for Learning Latent Variable Models Journal of Machine Learning Research; Vol. 15;

Anandkumar, Animashree; Ge, Rong et al. (2014) A Tensor Approach to Learning Mixed Membership Community Models Journal of Machine Learning Research; Vol. 15;

Sattari, Pegah; Kurant, Maciej et al. (2014) Active Learning of Multiple Source Multiple Destination Topologies IEEE Transactions on Signal Processing; Vol. 62; No. 8;

Janzamin, Majid; Anandkumar, Animashree (2014) High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models Journal of Machine Learning Research; Vol. 15;

Anandkumar, Animashree; He, Ting et al. (2013) Seeing through black boxes: Tracking transactions through queues under monitoring resource constraints Performance Evaluation; Vol. 70; No. 12;

Anandkumar, Animashree; Hassidim, Avinatan et al. (2013) Topology discovery of sparse random graphs with few participants Random Structures & Algorithms; Vol. 43; No. 1;

Anandkumar, Animashree; Valluvan, Ragupathyraj (2013) Learning loopy graphical models with latent variables: Efficient methods and guarantees Annals of Statistics; Vol. 41; No. 2;

Liu, Ying; Chandrasekaran, Venkat et al. (2012) Feedback Message Passing for Inference in Gaussian Graphical Models IEEE Transactions on Signal Processing; Vol. 60; No. 8;

Anandkumar, Animashree; Tan, Vincent Y. F. et al. (2012) High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion Journal of Machine Learning Research; Vol. 13;

Anandkumar, Animashree; Tan, Vincent Y. F. et al. (2012) High-dimensional structure estimation in Ising models: Local separation criterion Annals of Statistics; Vol. 40; No. 3;

Anandkumar, Amod J. G.; Anandkumar, Animashree et al. (2011) Robust Rate Maximization Game Under Bounded Channel Uncertainty IEEE Transactions on Vehicular Technology; Vol. 60; No. 9; 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, 14-19 March 2010 , Dallas, TX

Tan, Vincent Y. F.; Anandkumar, Animashree et al. (2011) Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates Journal of Machine Learning Research; Vol. 12;

Choi, Myung Jin; Tan, Vincent Y. F. et al. (2011) Learning Latent Tree Graphical Models Journal of Machine Learning Research; Vol. 12;

Anandkumar, Animashree; Michael, Nithin et al. (2011) Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret IEEE Journal on Selected Areas in Communications; Vol. 29; No. 4; 2010 IEEE INFOCOM, 15-19 March 2010 , San Diego, CA

Tan, Vincent Y. F.; Anandkumar, Animashree et al. (2011) A Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov Tree Structures IEEE Transactions on Information Theory; Vol. 57; No. 3; 2009 International Symposium on Information Theory (ISIT), 28 June - 3 July 2009 , Seoul, Korea

Tan, Vincent Y. F.; Anandkumar, Animashree et al. (2010) Learning Gaussian Tree Models: Analysis of Error Exponents and Extremal Structures IEEE Transactions on Signal Processing; Vol. 58; No. 5; 47th Annual Allerton Conference on Communication, Control, and Computing, 2009. Allerton 2009, September 30 - October 2, 2009 , Monticello, IL

Anandkumar, Animashree; Yukich, Joseph E. et al. (2009) Energy scaling laws for distributed inference in random fusion networks IEEE Journal on Selected Areas in Communications; Vol. 27; No. 7;

Anandkumar, Animashree; Tong, Lang et al. (2009) Detection of Gauss-Markov Random Fields With Nearest-Neighbor Dependency IEEE Transactions on Information Theory; Vol. 55; No. 2; 2007 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '07), 15-20 April 2007 , Honolulu, HI

Anandkumar, Animashree; Tong, Lang et al. (2008) Optimal Node Density for Detection in Energy-Constrained Random Networks IEEE Transactions on Signal Processing; Vol. 56; No. 10; 45th Allerton Conference on Communication, Control and Computing, 26-28 September 2007 , Monticello, IL

Anandkumar, Animashree; Tong, Lang et al. (2008) Distributed Estimation Via Random Access IEEE Transactions on Information Theory; Vol. 54; No. 7;

Anandkumar, Animashree; Tong, Lang (2007) Type-Based Random Access for Distributed Detection Over Multiaccess Fading Channels IEEE Transactions on Signal Processing; Vol. 55; No. 10;