Anandkumar, Animashree

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• Xiao, Chaowei; Chen, Zhongzhu; et el. (2022) DensePure: Understanding Diffusion Models towards Adversarial Robustness; 10.48550/arXiv.2211.00322
• Qiao, Zhuoran; Nie, Weili; et el. (2022) Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models; 10.48550/arXiv.2209.15171
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• Huang, De-An; Yu, Zhiding; et el. (2022) MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training; 10.48550/arXiv.2208.02245
• Sedghi, Hanie; Anandkumar, Anima; et el. (2022) Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Noisy Matrix Decomposition; 10.48550/arXiv.1402.5131
• Netrapalli, Praneeth; Niranjan, U N; et el. (2022) Non-convex Robust PCA; 10.48550/arXiv.1410.7660
• Jeong, Yoonwoo; Shin, Seungjoo; et el. (2022) PeRFception: Perception using Radiance Fields; 10.48550/arXiv.2208.11537
• Wang, Zichao; Nie, Weili; et el. (2022) Retrieval-based Controllable Molecule Generation; 10.48550/arXiv.2208.11126
• Cao, Yulong; Xu, Danfei; et el. (2022) Robust Trajectory Prediction against Adversarial Attacks; 10.48550/arXiv.2208.00094
• Ma, Runzhuo; Ramaswamy, Ashwin; et el. (2022) Surgical gestures as a method to quantify surgical performance and predict patient outcomes; npj Digital Medicine; Vol. 5; Art. No. 187; PMCID PMC9780308; 10.1038/s41746-022-00738-y
• Shu, Manli; Nie, Weili; et el. (2022) Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models; 10.48550/arXiv.2209.07511
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• Zhao, Jiawei; Dai, Steve; et el. (2022) LNS-Madam: Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update; IEEE Transactions on Computers; Vol. 71; No. 12; 3179-3190; 10.1109/tc.2022.3202747
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• Schäfer, Florian; Anandkumar, Anima; et el. (2020) Competitive Mirror Descent; 10.48550/arXiv.2006.10179
• Prajapat, Manish; Azizzadenesheli, Kamyar; et el. (2020) Competitive Policy Optimization; 10.48550/arXiv.2006.10611
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• Lale, Sahin; Azizzadenesheli, Kamyar; et el. (2020) Explore More and Improve Regret in Linear Quadratic Regulators; 10.48550/arXiv.2007.12291
• Li, Zongyi; Kovachki, Nikola; et el. (2020) Fourier Neural Operator for Parametric Partial Differential Equations; 10.48550/arXiv.2010.08895
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• Anandkumar, Anima and Sedghi, Hanie (2019) Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods; 10.48550/arXiv.1503.04567
• Nimmagadda, Tejaswi and Anandkumar, Anima (2019) Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models; 10.48550/arXiv.1505.00308
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• Anandkumar, Anima and Ge, Rong (2019) Efficient approaches for escaping higher order saddle points in non-convex optimization; 10.48550/arXiv.1602.05908
• Arabshahi, Forough and Anandkumar, Animashree (2019) Spectral Methods for Correlated Topic Models; 10.48550/arXiv.1605.09080
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• Lale, Sahin; Azizzadenesheli, Kamyar; et el. (2019) Stochastic Linear Bandits with Hidden Low Rank Structure; 10.48550/arXiv.1901.09490
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• Arabshahi, Forough; Singh, Sameer; et el. (2018) Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs; 10.48550/arXiv.1801.04342
• Khetan, Ashish; Lipton, Zachary C.; et el. (2018) Learning From Noisy Singly-labeled Data; 10.48550/arXiv.1712.04577
• Dhillon, Guneet S.; Azizzadenesheli, Kamyar; et el. (2018) Stochastic Activation Pruning for Robust Adversarial Defense; 10.48550/arXiv.1803.01442
• Shi, Yang; Furlanello, Tommaso; et el. (2018) Question Type Guided Attention in Visual Question Answering; ISBN 978-3-030-01224-3; Computer Vision – ECCV 2018; Springer Nature: Cham, Switzerland; 158-175; 10.1007/978-3-030-01225-0_10
• Shen, Yanyao; Yun, Hyokun; et el. (2018) Deep Active Learning for Named Entity Recognition; 10.48550/arXiv.1707.05928
• Azizzadenesheli, Kamyar; Brunskill, Emma; et el. (2018) Efficient Exploration Through Bayesian Deep Q-Networks; ISBN 9781728101248; 2018 Information Theory and Applications Workshop (ITA); IEEE: Piscataway, NJ; 1-9; 10.1109/ita.2018.8503252
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• Agarwal, Alekh; Anandkumar, Animashree; et el. (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; Ge, Rong; et el. (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; Anandkumar, Animashree; et el. (2016) Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization; SIAM Journal of Optimization; Vol. 26; No. 4; 2775-2799; 10.1137/140979861
• Wang, Yining and Anandkumar, Animashree (2016) Online and Differentially-Private Tensor Decomposition; ISBN 9781510838819; Neural Information Processing Systems 2016; Neural Information Processing Systems Foundation: La Jolla, CA; Art. No. 6498; 10.48550/arXiv.1606.06237
• Shi, Yang; Niranjan, U. N.; et el. (2016) Tensor Contractions with Extended BLAS Kernels on CPU and GPU; ISBN 978-1-5090-5411-4; 2016 IEEE 23rd International Conference on High Performance Computing; IEEE: Piscataway, NJ; 193-202; 10.1109/HiPC.2016.031
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• Azizzadenesheli, Kamyar; Lazaric, Alessandro; et el. (2016) Reinforcement Learning of POMDPs using Spectral Methods; Proceedings of Machine Learning Research; Vol. 49; 193-256; 10.48550/arXiv.1602.07764
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• Huang, Furong; Niranjan, U. N.; et el. (2015) Online Tensor Methods for Learning Latent Variable Models; Journal of Machine Learning Research; Vol. 16; 2797-2835; 10.48550/arXiv.1309.0787
• Anandkumar, Animashree; Hsu, Daniel; et el. (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
• Arabshahi, Forough; Huang, Furong; et el. (2015) Are You Going to the Party: Depends, Who Else is Coming?: [Learning Hidden Group Dynamics via Conditional Latent Tree Models]; ISBN 978-1-4673-9504-5; 2015 IEEE International Conference on Data Mining; IEEE: Piscataway, NJ; 697-702; 10.1109/ICDM.2015.146
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• Anandkumar, Animashree; Foster, Dean P.; et el. (2015) A Spectral Algorithm for Latent Dirichlet Allocation; Algorithmica; Vol. 72; No. 1; 193-214; 10.1007/s00453-014-9909-1
• Janzamin, Majid; Sedghi, Hanie; et el. (2015) Score Function Features for Discriminative Learning; 10.48550/arXiv.1412.6514
• Sedghi, Hanie and Anandkumar, Anima (2014) Provable Methods for Training Neural Networks with Sparse Connectivity; 10.48550/arXiv.1412.2693
• Sedghi, Hanie; Janzamin, Majid; et el. (2014) Provable Tensor Methods for Learning Mixtures of Generalized Linear Models; Proceedings of Machine Learning Research; Vol. 51; 1223-1231; 10.48550/arXiv.1412.3046
• Sedghi, Hanie and Anandkumar, Anima (2014) Provable Methods for Training Neural Networks with Sparse Connectivity; 10.48550/arXiv.1412.2693
• Sedghi, Hanie; Anandkumar, Anima; et el. (2014) Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition; ISBN 9781510800410; Advances in neural information processing systems 27 : 28th Annual Conference on Neural Information Processing Systems 2014; Neural Information Processing Foundation: La Jolla, CA; 1-9
• Netrapalli, Praneeth; Niranjan, U N; et el. (2014) Provable Non-convex Robust PCA; ISBN 9781510800410; Advances in neural information processing systems 27 : 28th Annual Conference on Neural Information Processing Systems 2014; Neural Information Processing Foundation: La Jolla, CA; 1-9
• Anandkumar, Animashree; Ge, Rong; et el. (2014) Tensor Decompositions for Learning Latent Variable Models; Journal of Machine Learning Research; Vol. 15; 2773-2832; 10.48550/arXiv.1210.7559
• Anandkumar, Animashree; Ge, Rong; et el. (2014) A Tensor Approach to Learning Mixed Membership Community Models; Journal of Machine Learning Research; Vol. 15; 2239-2312; 10.48550/arXiv.1302.2684
• Sattari, Pegah; Kurant, Maciej; et el. (2014) Active Learning of Multiple Source Multiple Destination Topologies; IEEE Transactions on Signal Processing; Vol. 62; No. 8; 1926-1937; 10.1109/TSP.2014.2304431
• 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; He, Ting; et el. (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; Hassidim, Avinatan; et el. (2013) Topology discovery of sparse random graphs with few participants; Random Structures & Algorithms; Vol. 43; No. 1; 16-48; 10.1002/rsa.20420
• Anandkumar, Amod J. G.; Anandkumar, Animashree; et el. (2013) Robust noncooperative rate-maximization game for MIMO Gaussian interference channels under bounded channel uncertainty; ISBN 978-1-4799-0356-6; 2013 IEEE International Conference on Acoustics, Speech and Signal Processing; IEEE: Piscataway, NJ; 4819-4823; 10.1109/ICASSP.2013.6638576
• Huang, Furong and Anandkumar, Animashree (2013) FCD: Fast-concurrent-distributed load balancing under switching costs and imperfect observations; ISBN 978-1-4673-5944-3; 2013 Proceedings IEEE INFOCOM; IEEE: Piscataway, NJ; 1896-1904; 10.1109/INFCOM.2013.6566989
• Sattari, Pegah; Kurant, Maciej; et el. (2013) Active learning of multiple source multiple destination topologies; ISBN 978-1-4673-5237-6; 47th Annual Conference on Information Sciences and Systems; IEEE: Piscataway, NJ; 1-6; 10.1109/CISS.2013.6552253
• 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, Anima; Foster, Dean P.; et el. (2012) A Spectral Algorithm for Latent Dirichlet Allocation
• Anandkumar, Anima and Valluvan, Ragupathyraj (2012) Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs; ISBN 9781627480031; Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012; Neural Information Processing Foundation: La Jolla, CA; 1-9
• Liu, Ying; Chandrasekaran, Venkat; et el. (2012) Feedback Message Passing for Inference in Gaussian Graphical Models; IEEE Transactions on Signal Processing; Vol. 60; No. 8; 4135-4150; 10.1109/TSP.2012.2195656
• Anandkumar, Animashree; Tan, Vincent Y. F.; et el. (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
• Anandkumar, Animashree; Tan, Vincent Y. F.; et el. (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, Anima; Tan, Voncent Y. F.; et el. (2011) High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions; ISBN 9781618395993; Advances in neural information processing systems 24 : 25th Annual Conference on Neural Information Processing Systems 2011, December 12-15, 2011, Granada, Spain; Neural Information Processing Foundation: La Jolla, CA; 1-9
• Anandkumar, Animashree; Hsu, Daniel; et el. (2011) Learning Mixtures of Tree Graphical Models; ISBN 9781618395993; Advances in neural information processing systems 24 : 25th Annual Conference on Neural Information Processing Systems 2011; Neural Information Processing Foundation: La Jolla, CA; 1-9
• Anandkumar, Animashree; Chaudhuri, Kamalika; et el. (2011) Spectral Methods for Learning Multivariate Latent Tree Structure; ISBN 9781618395993; Advances in neural information processing systems 24 : 25th Annual Conference on Neural Information Processing Systems 2011, December 12-15, 2011, Granada, Spain; Neural Information Processing Foundation: La Jolla, CA; 1-9
• Anandkumar, Amod J. G.; Anandkumar, Animashree; et el. (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
• Anandkumar, Animashree; Chaudhuri, Kamalika; et el. (2011) Spectral Methods for Learning Multivariate Latent Tree Structure; 10.48550/arXiv.1107.1283
• Khajehnejad, M. Amin; Yoo, Juhwan; et el. (2011) Summary Based Structures with Improved Sublinear Recovery for Compressed Sensing; ISBN 978-1-4577-0596-0; 2011 IEEE International Symposium on Information Theory Proceedings; IEEE: Piscataway, NJ; 1427-1431; 10.1109/ISIT.2011.6033775
• Tan, Vincent Y. F.; Anandkumar, Animashree; et el. (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; Tan, Vincent Y. F.; et el. (2011) Learning Latent Tree Graphical Models; Journal of Machine Learning Research; Vol. 12; 1771-1812; 10.48550/arXiv.1009.2722
• Anandkumar, Animashree; Michael, Nithin; et el. (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
• Balister, Paul; Bollobás, Béla; et el. (2011) Energy-latency tradeoff for in-network function computation in random networks; ISBN 978-1-4244-9919-9; 2011 Proceedings IEEE INFOCOM; IEEE: Piscataway, NJ; 1575-1583; 10.1109/INFCOM.2011.5934949
• He, Ting; Anandkumar, Animashree; et el. (2011) Index-based sampling policies for tracking dynamic networks under sampling constraints; ISBN 978-1-4244-9919-9; 2011 Proceedings IEEE INFOCOM; IEEE: Piscataway, NJ; 1233-1241; 10.1109/INFCOM.2011.5934904
• Tan, Vincent Y. F.; Anandkumar, Animashree; et el. (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
• Anandkumar, Amod J. G.; Anandkumar, Animashree; et el. (2010) Efficiency of rate-maximization game under bounded channel uncertainty; ISBN 978-1-4244-9722-5; 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers; IEEE: Piscataway, NJ; 482-486; 10.1109/ACSSC.2010.5757605
• Tan, Vincent Y. F.; Anandkumar, Animashree; et el. (2010) Error exponents for composite hypothesis testing of Markov forest distributions; ISBN 978-1-4244-7890-3; 2010 IEEE International Symposium on Information Theory; IEEE: Piscataway, NJ; 1613-1617; 10.1109/ISIT.2010.5513399
• Liu, Ying; Chandrasekaran, Venkat; et el. (2010) Feedback Message Passing for Inference in Gaussian Graphical Models; ISBN 978-1-4244-6960-4; 2010 IEEE International Symposium on Information Theory Proceedings (ISIT); IEEE: Piscataway, NJ; 1683-1687; 10.1109/ISIT.2010.5513321
• Anandkumar, Animashree; Yukich, Joseph; et el. (2010) Limit laws for random spatial graphical models; ISBN 978-1-4244-7890-3; 2010 IEEE International Symposium on Information Theory; IEEE: Piscataway, NJ; 1728-1732; 10.1109/ISIT.2010.5513254
• Tan, Vincent Y. F.; Anandkumar, Animashree; et el. (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; Michael, Nithin; et el. (2010) Opportunistic Spectrum Access with Multiple Users: Learning under Competition; ISBN 978-1-4244-5836-3; 2010 Proceedings IEEE INFOCOM; IEEE: Piscataway, NJ; 1-9; 10.1109/INFCOM.2010.5462144
• Anandkumar, Amod J. G.; Anandkumar, Animashree; et el. (2010) Robust rate-maximization game under bounded channel uncertainty; ISBN 978-1-4244-4295-9; 2010 IEEE International Conference on Acoustics, Speech and Signal Processing; IEEE: Piscataway, NJ; 3158-3161; 10.1109/ICASSP.2010.5496066
• Tan, Vincent Y. F.; Anandkumar, Animashree; et el. (2009) How do the structure and the parameters of Gaussian tree models affect structure learning?; ISBN 978-1-4244-5870-7; 47th Annual Allerton Conference on Communication, Control, and Computing; IEEE: Piscataway, NJ; 684-691; 10.1109/ALLERTON.2009.5394929
• Anandkumar, Animashree; Yukich, Joseph E.; et el. (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
• Tan, Vincent Y. F.; Anandkumar, Animashree; et el. (2009) A large-deviation analysis for the maximum likelihood learning of tree structures; ISBN 978-1-4244-4312-3; 2009 IEEE International Symposium on Information Theory; IEEE: Piscataway, NJ; 1140-1144; 10.1109/ISIT.2009.5206012
• Anandkumar, Animashree; Tong, Lang; et el. (2009) Detection error exponent for spatially dependent samples in random networks; ISBN 978-1-4244-4312-3; 2009 IEEE International Symposium on Information Theory; IEEE: Piscataway, NJ; 2882-2886; 10.1109/ISIT.2009.5205358
• Anandkumar, Animashree; Wang, Meng; et el. (2009) Prize-Collecting Data Fusion for Cost-Performance Tradeoff in Distributed Inference; ISBN 978-1-4244-3512-8; 28th IEEE Conference on Computer Communications; IEEE: Piscataway, NJ; 2150-2158; 10.1109/INFCOM.2009.5062139
• Anandkumar, Animashree; Tong, Lang; et el. (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; Tong, Lang; et el. (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
• Ezovski, G. Matthew; Anandkumar, Animashree; et el. (2008) Min-min times in peer-to-peer file sharing networks; ISBN 978-1-4244-2925-7; 46th Annual Allerton Conference on Communication, Control, and Computing; IEEE: Piscataway, NJ; 1487-1494; 10.1109/ALLERTON.2008.4797738
• Anandkumar, Animashree; Tong, Lang; et el. (2008) Distributed Estimation Via Random Access; IEEE Transactions on Information Theory; Vol. 54; No. 7; 3175-3181; 10.1109/TIT.2008.924652
• Anandkumar, Animashree; Bisdikian, Chatschik; et el. (2008) Tracking in a spaghetti bowl: monitoring transactions using footprints; ISBN 978-1-60558-005-0; Proceedings of the 2008 ACM SIGMETRICS international conference on measurement and modeling of computer systems; Association for Computing Machinery: New York, NY; 133-144; 10.1145/1375457.1375473
• Anandkumar, Animashree; Tong, Lang; et el. (2008) Minimum Cost Data Aggregation with Localized Processing for Statistical Inference; ISBN 978-1-4244-2025-4; 27th IEEE Conference on Computer Communications; IEEE: Piscataway, NJ; 1454-1462; 10.1109/INFOCOM.2008.129
• Sengupta, Bikram; Banerjee, Nilanjan; et el. (2008) Non-intrusive transaction monitoring using system logs; ISBN 978-1-4244-2065-0; 2008 IEEE Network Operations and Management Symposium; IEEE: Piscataway, NJ; 879-882; 10.1109/NOMS.2008.4575237
• 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
• Anandkumar, Animashree; Tong, Lang; et el. (2007) Detection of Gauss-Markov Random Field on Nearest-Neighbor Graph; ISBN 1-4244-0727-3; 2007 IEEE International Conference on Acoustics, Speech and Signal Processing; IEEE: Piscataway, NJ; 829-832; 10.1109/ICASSP.2007.366808
• Anandkumar, Animashree; Tong, Lang; et el. (2007) Energy Efficient Routing for Statistical Inference of Markov Random Fields; ISBN 9781424410361; 2007 41st annual conference on information sciences and systems : Baltimore, MD, 14-16 March, 2007.; IEEE: Piscataway, NJ; 643-648; 10.1109/CISS.2007.4298386
• Anandkumar, Animashree and Tong, Lang (2006) A Large Deviation Analysis of Detection Over Multi-Access Channels with Random Number of Sensors; ISBN 1-4244-0469-X; 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings; IEEE: Piscataway, NJ; 1097-1100; 10.1109/ICASSP.2006.1661164
• Anandkumar, Animashree and Tong, Lang (2006) Distributed Statistical Inference using Type Based Random Access over Multi-access Fading Channels; ISBN 1-4244-0349-9; 2006 40th Annual Conference on Information Sciences and Systems; IEEE: Piscataway, NJ; 38-43; 10.1109/CISS.2006.286427