Anandkumar, Animashree

Article from CaltechAUTHORS

• Lale, Sahin and Renn, Peter I., el al. (2024) FALCON: Fourier Adaptive Learning and Control for Disturbance Rejection Under Extreme Turbulence; npj Robotics; Vol. 2; No. 1; 6; 10.1038/s44182-024-00013-0
• McClain Gomez, Abigail and Patti, Taylor L., el al. (2024) Near-term distributed quantum computation using mean-field corrections and auxiliary qubits; Quantum Science and Technology; Vol. 9; No. 3; 035022; 10.1088/2058-9565/ad3f45
• Gopakumar, Vignesh and Pamela, Stanislas, el al. (2024) Plasma surrogate modelling using Fourier neural operators; Nuclear Fusion; Vol. 64; No. 5; 056025; 10.1088/1741-4326/ad313a
• Azizzadenesheli, Kamyar and Kovachki, Nikola, el al. (2024) Neural operators for accelerating scientific simulations and design; Nature Reviews Physics; 10.1038/s42254-024-00712-5
• Li, Zongyi and Zheng, Hongkai, el al. (2024) Physics-Informed Neural Operator for Learning Partial Differential Equations; ACM / IMS Journal of Data Science; 10.1145/3648506
• Qiao, Zhuoran and Nie, Weili, el al. (2024) State-specific protein–ligand complex structure prediction with a multiscale deep generative model; Nature Machine Intelligence; 10.1038/s42256-024-00792-z
• Zhou, Tingtao and Wan, Xuan, el al. (2024) AI-aided geometric design of anti-infection catheters; Science Advances; Vol. 10; No. 1; eadj1741; PMCID PMC10776022; 10.1126/sciadv.adj1741
• Zheng, Zhiling and Alawadhi, Ali H., el al. (2023) Shaping the Water-Harvesting Behavior of Metal–Organic Frameworks Aided by Fine-Tuned GPT Models; Journal of the American Chemical Society; Vol. 145; No. 51; 28284-28295; 10.1021/jacs.3c12086
• Feng, Jie and Shi, Yuanyuan, el al. (2023) Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage Control; IEEE Transactions on Control of Network Systems; 1-12; 10.1109/tcns.2023.3338240
• Liu, Shengchao and Nie, Weili, el al. (2023) Multi-modal molecule structure–text model for text-based retrieval and editing; Nature Machine Intelligence; Vol. 5; No. 12; 1447-1457; 10.1038/s42256-023-00759-6
• Kiyasseh, Dani and Ma, Runzhuo, el al. (2023) A vision transformer for decoding surgeon activity from surgical videos; Nature Biomedical Engineering; Vol. 7; No. 6; 780-796; PMCID PMC10307635; 10.1038/s41551-023-01010-8
• Kiyasseh, Dani and Laca, Jasper, el al. (2023) Human visual explanations mitigate bias in AI-based assessment of surgeon skills; npj Digital Medicine; Vol. 6; Art. No. 54; PMCID PMC10063676; 10.1038/s41746-023-00766-2
• Wen, Gege and Li, Zongyi, el al. (2023) Real-time high-resolution CO₂ geological storage prediction using nested Fourier neural operators; Energy and Environmental Science; Vol. 16; No. 4; 1732-1741; 10.1039/d2ee04204e
• Inouye, Daniel A. and Ma, Runzhuo, el al. (2023) Assessing the efficacy of dissection gestures in robotic surgery; Journal of Robotic Surgery; Vol. 17; No. 2; 597-603; 10.1007/s11701-022-01458-x
• Kiyasseh, Dani and Laca, Jasper, el al. (2023) A multi-institutional study using artificial intelligence to provide reliable and fair feedback to surgeons; Communications Medicine; Vol. 3; Art. No. 42; PMCID PMC10063640; 10.1038/s43856-023-00263-3
• Hung, Andrew J. and Bao, Richard, el al. (2023) Capturing fine-grained details for video-based automation of suturing skills assessment; International Journal of Computer Assisted Radiology and Surgery; Vol. 18; No. 3; 545-552; PMCID PMC9975072; 10.1007/s11548-022-02778-x
• Dommer, Abigail and Casalino, Lorenzo, el al. (2023) #COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol; International Journal of High Performance Computing Applications; Vol. 37; No. 1; 28-44; PMCID PMC9527558; 10.1177/10943420221128233
• Ma, Runzhuo and Ramaswamy, Ashwin, el al. (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
• Zhao, Jiawei and Dai, Steve, el al. (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
• Laca, Jasper A. and Kocielnik, Rafal, el al. (2022) Using Real-time Feedback To Improve Surgical Performance on a Robotic Tissue Dissection Task; European Urology Open Science; Vol. 46; 15-21; PMCID PMC9732447; 10.1016/j.euros.2022.09.015
• Trifan, Anda and Gorgun, Defne, el al. (2022) Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action; International Journal of High Performance Computing Applications; 10.1177/10943420221113513
• Hoeller, David and Rudin, Nikita, el al. (2022) Neural Scene Representation for Locomotion on Structured Terrain; IEEE Robotics and Automation Letters; Vol. 7; No. 4; 8667-8674; 10.1109/LRA.2022.3184779
• Pangal, Dhiraj J. and Kugener, Guillaume, el al. (2022) Use of surgical video–based automated performance metrics to predict blood loss and success of simulated vascular injury control in neurosurgery: a pilot study; Journal of Neurosurgery; Vol. 137; No. 3; 840-849; 10.3171/2021.10.jns211064
• Markarian, Nicholas and Kugener, Guillaume, el al. (2022) Validation of Machine Learning-Based Automated Surgical Instrument Annotation Using Publicly Available Intraoperative Video; Operative Neurosurgery; Vol. 23; No. 3; 235-240; 10.1227/ons.0000000000000274
• Patti, Taylor L. and Kossaifi, Jean, el al. (2022) Variational quantum optimization with multibasis encodings; Physical Review Research; Vol. 4; No. 3; Art. No. 4.033142; 10.1103/physrevresearch.4.033142
• Qiao, Zhuoran and Christensen, Anders S., el al. (2022) Informing geometric deep learning with electronic interactions to accelerate quantum chemistry; Proceedings of the National Academy of Sciences; Vol. 119; No. 31; Art. No. e2205221119; PMCID PMC9351474; 10.1073/pnas.2205221119
• Xu, Pan and Zheng, Hongkai, el al. (2022) Langevin Monte Carlo for Contextual Bandits; Proceedings of Machine Learning Research; Vol. 162; 24830-24850; 10.48550/arXiv.arXiv.2206.11254
• Kargin, Taylan and Lale, Sahin, el al. (2022) Thompson Sampling Achieves Õ(√T) Regret in Linear Quadratic Control; Proceedings of Machine Learning Research; Vol. 178; 3235-3284; 10.48550/arXiv.2206.08520
• Kugener, Guillaume and Zhu, Yichao, el al. (2022) Deep Neural Networks Can Accurately Detect Blood Loss and Hemorrhage Control Task Success From Video; Neurosurgery; Vol. 90; No. 6; 823-829; 10.1227/neu.0000000000001906
• Pangal, Dhiraj J. and Kugener, Guillaume, el al. (2022) Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1 min of video; Scientific Reports; Vol. 12; Art. No. 8137; PMCID PMC9114003; 10.1038/s41598-022-11549-2
• Nie, Weili and Guo, Brandon, el al. (2022) Diffusion Models for Adversarial Purification; Proceedings of Machine Learning Research; Vol. 162; 16805-16827; 10.48550/arXiv.2205.07460
• O'Connell, Michael and Shi, Guanya, el al. (2022) Neural-Fly enables rapid learning for agile flight in strong winds; Science Robotics; Vol. 7; No. 66; Art. No. eabm6597; 10.1126/scirobotics.abm6597
• Roberts, Sidney I. and Cen, Steven Y., el al. (2022) The Relationship Between Technical Skills, Cognitive Workload, and Errors During Robotic Surgical Exercises; Journal of Endourology; Vol. 36; No. 5; 712-720; PMCID PMC9145254; 10.1089/end.2021.0790
• Wen, Gege and Li, Zongyi, el al. (2022) U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow; Advances in Water Resources; Vol. 163; Art. No. 104180; 10.1016/j.advwatres.2022.104180
• Zhou, Daquan and Yu, Zhiding, el al. (2022) Understanding The Robustness in Vision Transformers; Proceedings of Machine Learning Research; Vol. 162; 27378-27394; 10.48550/arXiv.2204.12451
• Kugener, Guillaume and Pangal, Dhiraj J., el al. (2022) Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications; JAMA Network Open; Vol. 5; No. 3; Art. No. e223177; PMCID PMC8938712; 10.1001/jamanetworkopen.2022.3177
• Liu, Burigede and Kovachki, Nikola, el al. (2022) A learning-based multiscale method and its application to inelastic impact problems; Journal of the Mechanics and Physics of Solids; Vol. 158; Art. No. 104668; 10.1016/j.jmps.2021.104668
• Christensen, Anders S. and Sirumalla, Sai Krishna, el al. (2021) OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy; Journal of Chemical Physics; Vol. 155; No. 20; Art. No. 204103; 10.1063/5.0061990
• Lee, Youngwoon and Lim, Joseph J., el al. (2021) Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization; Proceedings of Machine Learning Research; Vol. 164; 406-416; 10.48550/arXiv.arXiv.2111.07999
• Hung, Andrew J. and Liu, Yan, el al. (2021) Deep Learning to Automate Technical Skills Assessment in Robotic Surgery; JAMA Surgery; Vol. 156; No. 11; 1059-1060; 10.1001/jamasurg.2021.3651
• Chan, Justin and Pangal, Dhiraj J., el al. (2021) A systematic review of virtual reality for the assessment of technical skills in neurosurgery; Neurosurgical Focus; Vol. 51; No. 2; Art. No. E15; 10.3171/2021.5.focus21210
• Fan, Linxi and Wang, Guanzhi, el al. (2021) SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies; Proceedings of Machine Learning Research; Vol. 139; 3088-3099; 10.48550/arXiv.2106.09678
• Chang, Nadine and Yu, Zhiding, el al. (2021) Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection; Proceedings of Machine Learning Research; Vol. 139; 1463-1472; 10.48550/arXiv.2104.05702
• Mahajan, Anuj and Samvelyan, Mikayel, el al. (2021) Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning; Proceedings of Machine Learning Research; Vol. 139; 7301-7312; 10.48550/arXiv.2106.00136
• Liu, Bo and Liu, Qiang, el al. (2021) Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition; Proceedings of Machine Learning Research; Vol. 139; 6860-6870; 10.48550/arXiv.2105.08692
• Yu, Jing and Gehring, Clement, el al. (2021) Robust Reinforcement Learning: A Constrained Game-theoretic Approach; Proceedings of Machine Learning Research; Vol. 144; 1242-1254
• Lale, Sahin and Azizzadenesheli, Kamyar, el al. (2021) Finite-time System Identification and Adaptive Control in Autoregressive Exogenous Systems; Proceedings of Machine Learning Research; Vol. 144; 967-979
• Qu, Guannan and Shi, Yuanyuan, el al. (2021) Stable Online Control of Linear Time-Varying Systems; Proceedings of Machine Learning Research; Vol. 144; 742-753; 10.48550/arXiv.2104.14134
• Lale, Sahin and Teke, Oguzhan, el al. (2021) Stability and Identification of Random Asynchronous Linear Time-Invariant Systems; Proceedings of Machine Learning Research; Vol. 144; 651-663; 10.48550/arXiv.2012.04160
• Luongo, Francisco and Hakim, Ryan, el al. (2021) Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery; Surgery; Vol. 169; No. 5; 1240-1244; PMCID PMC7994208; 10.1016/j.surg.2020.08.016
• Panagakis, Yannis and Kossaifi, Jean, el al. (2021) Tensor Methods in Computer Vision and Deep Learning; Proceedings of the IEEE; Vol. 109; No. 5; 863-890; 10.1109/jproc.2021.3074329
• Kashinath, K. and Mustafa, M., el al. (2021) Physics-informed machine learning: case studies for weather and climate modelling; Philosophical Transactions A: Mathematical, Physical and Engineering Sciences; Vol. 379; No. 2194; Art. No. 20200093; 10.1098/rsta.2020.0093
• Zhao, Eric and Liu, Anqi, el al. (2021) Active Learning under Label Shift; Proceedings of Machine Learning Research; Vol. 130; 3412-3420; 10.48550/arXiv.2007.08479
• Nakka, Yashwanth Kumar and Liu, Anqi, el al. (2021) Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems; IEEE Robotics and Automation Letters; Vol. 6; No. 2; 389-396; 10.1109/LRA.2020.3044033
• Chu, Linda C. and Anandkumar, Animashree, el al. (2020) The Potential Dangers of Artificial Intelligence for Radiology and Radiologists; Journal of the American College of Radiology; Vol. 17; No. 10; 1309-1311; PMCID PMC7164850; 10.1016/j.jacr.2020.04.010
• Qiao, Zhuoran and Welborn, Matthew, el al. (2020) OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features; Journal of Chemical Physics; Vol. 153; No. 12; Art. No. 124111; 10.1063/5.0021955
• Ren, Hongyu and Zhu, Yuke, el al. (2020) OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation; Proceedings of Machine Learning Research; Vol. 124; 1378-1387; 10.48550/arXiv.2008.07087
• Kossaifi, Jean and Lipton, Zachary C., el al. (2020) Tensor Regression Networks; Journal of Machine Learning Research; Vol. 21; 1-21; 10.48550/arXiv.1707.08308
• Chen, Wuyang and Yu, Zhiding, el al. (2020) Automated Synthetic-to-Real Generalization; Proceedings of Machine Learning Research; Vol. 119; 1746-1756; 10.48550/arXiv.2007.06965
• Chen, Beidi and Liu, Weiyang, el al. (2020) Angular Visual Hardness; Proceedings of Machine Learning Research; Vol. 119; 1637-1648; 10.48550/arXiv.1912.02279
• Ross, Zachary E. and Trugman, Daniel T., el al. (2020) Directivity Modes of Earthquake Populations with Unsupervised Learning; Journal of Geophysical Research. Solid Earth; Vol. 125; No. 2; Art. No. e2019JB018299; 10.1029/2019JB018299
• Janzamin, Majid and Ge, Rong, el al. (2019) Spectral Learning on Matrices and Tensors; Foundations and Trends in Machine Learning; Vol. 12; No. 5-6; 393-536; 10.1561/2200000057
• Huang, Furong and Naresh, Niranjan Uma, el al. (2019) Guaranteed Scalable Learning of Latent Tree Models; Proceedings of Machine Learning Research; Vol. 115; 883-893; 10.48550/arXiv.1406.4566
• Cvitkovic, Milan and Singh, Badal, el al. (2019) Open Vocabulary Learning on Source Code with a Graph-Structured Cache; Proceedings of Machine Learning Research; Vol. 97; 1475-1485; 10.48550/arXiv.1810.08305
• Kwok, Roberta and Ranade, Gireeja, el al. (2019) Junior AI researchers are in demand by universities and industry; Nature; Vol. 568; No. 7753; 581-583; 10.1038/d41586-019-01248-w
• Kossaifi, Jean and Panagakis, Yannis, el al. (2019) TensorLy: Tensor Learning in Python; Journal of Machine Learning Research; Vol. 20; No. 26; 1-6; 10.48550/arXiv.1610.09555
• Bernstein, Jeremy and Wang, Yu-Xiang, el al. (2018) signSGD: Compressed Optimisation for Non-Convex Problems; Proceedings of Machine Learning Research; Vol. 80; 560-569; 10.48550/arXiv.1802.04434
• Furlanello, Tommaso and Lipton, Zachary C., el al. (2018) Born Again Neural Networks; Proceedings of Machine Learning Research; Vol. 80; 1607-1616; 10.48550/arXiv.1805.04770
• Tschannen, Michael and Khanna, Aran, el al. (2018) StrassenNets: Deep Learning with a Multiplication Budget; Proceedings of Machine Learning Research; Vol. 80; 4985-4994; 10.48550/arXiv.1712.03942
• Anandkumar, Anima and Deng, Yuan, el al. (2017) Homotopy Analysis for Tensor PCA; Proceedings of Machine Learning Research; Vol. 65; 79-104; 10.48550/arXiv.1610.09322
• 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
• Azizzadenesheli, Kamyar and Lazaric, Alessandro, el al. (2016) Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies; Proceedings of Machine Learning Research; Vol. 49; 1639-1642; 10.48550/arXiv.1608.04996
• Azizzadenesheli, Kamyar and Lazaric, Alessandro, el al. (2016) Reinforcement Learning of POMDPs using Spectral Methods; Proceedings of Machine Learning Research; Vol. 49; 193-256; 10.48550/arXiv.1602.07764
• 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
• Huang, Furong and Niranjan, U. N., el al. (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 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
• Sedghi, Hanie and Janzamin, Majid, el al. (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
• 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
• Sattari, Pegah and Kurant, Maciej, el al. (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 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
• Liu, Ying and Chandrasekaran, Venkat, el al. (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 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