Anandkumar, Animashree

Combined from CaltechAUTHORS

• McClain Gomez, Abigail and Patti, Taylor L., et el. (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, et el. (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, et el. (2024) Neural operators for accelerating scientific simulations and design; Nature Reviews Physics; 10.1038/s42254-024-00712-5
• Li, Zongyi and Zheng, Hongkai, et el. (2024) Physics-Informed Neural Operator for Learning Partial Differential Equations; ACM / IMS Journal of Data Science; 10.1145/3648506
• Qiao, Zhuoran and Nie, Weili, et el. (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, et el. (2024) AI-aided geometric design of anti-infection catheters; Science Advances; Vol. 10; No. 1; eadj1741; PMCID PMC10776022; 10.1126/sciadv.adj1741
• Luo, Zelun and Zou, Yuliang, et el. (2024) Differentially Private Video Activity Recognition; ISBN 979-8-3503-1892-0; 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV); 6643-6653; 10.1109/wacv57701.2024.00652
• Zheng, Zhiling and Alawadhi, Ali H., et el. (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, et el. (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
• Lale, Sahin and Shi, Yuanyuan, et el. (2023) KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Discrete-Time Systems; ISBN 979-8-3503-0124-3; 2023 62nd IEEE Conference on Decision and Control (CDC); 1334-1341; 10.1109/cdc49753.2023.10384011
• Liu, Shengchao and Nie, Weili, et el. (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
• Chen, Yilun and Yu, Zhiding, et el. (2023) FocalFormer3D : Focusing on Hard Instance for 3D Object Detection; ISBN 979-8-3503-0718-4; 2023 IEEE/CVF International Conference on Computer Vision (ICCV); 8360-8371; 10.1109/iccv51070.2023.00771
• Zhao, Bingyin and Yu, Zhiding, et el. (2023) Fully Attentional Networks with Self-emerging Token Labeling; ISBN 979-8-3503-0718-4; 2023 IEEE/CVF International Conference on Computer Vision (ICCV); 5562-5572; 10.1109/iccv51070.2023.00514
• Li, Yanwei and Yu, Zhiding, et el. (2023) End-to-end 3D Tracking with Decoupled Queries; ISBN 979-8-3503-0718-4; 2023 IEEE/CVF International Conference on Computer Vision (ICCV); 18256-18265; 10.1109/iccv51070.2023.01678
• Haghi, Benyamin and Ma, Lin, et el. (2023) EKGNet: A 10.96μW Fully Analog Neural Network for Intra-Patient Arrhythmia Classification; ISBN 979-8-3503-0026-0; 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS); 1-5; 10.1109/biocas58349.2023.10389164
• Choe, Jaesung and Choy, Christopher, et el. (2023) Spacetime Surface Regularization for Neural Dynamic Scene Reconstruction; ISBN 979-8-3503-0718-4; 2023 IEEE/CVF International Conference on Computer Vision (ICCV); 17825-17835; 10.1109/iccv51070.2023.01638
• Li, Zhiqi and Yu, Zhiding, et el. (2023) FB-BEV: BEV Representation from Forward-Backward View Transformations; ISBN 979-8-3503-0718-4; 2023 IEEE/CVF International Conference on Computer Vision (ICCV); 6896-6905; 10.1109/iccv51070.2023.00637
• Kiyasseh, Dani and Ma, Runzhuo, et el. (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
• Kurth, Thorsten and Subramanian, Shashank, et el. (2023) FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators; ISBN 9798400701900; PASC '23: Proceedings of the Platform for Advanced Scientific Computing Conference; 13; 10.1145/3592979.3593412
• Kiyasseh, Dani and Laca, Jasper, et el. (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
• Inouye, Daniel A. and Ma, Runzhuo, et el. (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
• Wen, Gege and Li, Zongyi, et el. (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
• Kiyasseh, Dani and Laca, Jasper, et el. (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
• Zvyagin, Maxim and Brace, Alexander, et el. (2023) GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics; PMCID PMC9709791; 10.1101/2022.10.10.511571
• Kocielnik, Rafal and Prabhumoye, Shrimai, et el. (2023) AutoBiasTest: Controllable Sentence Generation for Automated and Open-Ended Social Bias Testing in Language Models
• Xie, Chulin and Huang, De-An, et el. (2023) PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees
• Wang, Chen and Fan, Linxi, et el. (2023) MimicPlay: Long-Horizon Imitation Learning by Watching Human Play
• Renn, Peter I and Wang, Cong, et el. (2023) Forecasting subcritical cylinder wakes with Fourier Neural Operators
• Li, Yiming and Yu, Zhiding, et el. (2023) VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion
• Yang, Zhuolin and Ping, Wei, et el. (2023) Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning
• Lim, Jae Hyun and Kovachki, Nikola B., et el. (2023) Score-based Diffusion Models in Function Space
• Liu, Guan-Horng and Vahdat, Arash, et el. (2023) I²SB: Image-to-Image Schrödinger Bridge
• Liu, Shengchao and Nie, Weili, et el. (2023) Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
• Liu, Shengchao and Zhu, Yutao, et el. (2023) A Text-guided Protein Design Framework
• Liu, Shikun and Fan, Linxi, et el. (2023) Prismer: A Vision-Language Model with An Ensemble of Experts
• Lan, Shiyi and Yang, Xitong, et el. (2023) Vision Transformers Are Good Mask Auto-Labelers
• Anandkumar, Anima (2023) Neural Operators for Solving PDEs and Inverse Design; ISBN 9781450399784; ISPD '23: Proceedings of the 2023 International Symposium on Physical Design; 195; 10.1145/3569052.3578911
• Hung, Andrew J. and Bao, Richard, et el. (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, et el. (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
• Sedghi, Hanie and 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
• Gu, Jiaqi and Keller, Ben, et el. (2022) HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression; 10.48550/arXiv.2211.16749
• Wang, Zichao and Nie, Weili, et el. (2022) Retrieval-based Controllable Molecule Generation; 10.48550/arXiv.2208.11126
• Shu, Manli and Nie, Weili, et el. (2022) Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models; 10.48550/arXiv.2209.07511
• Liu, Mingjie and Yang, Haoyu, et el. (2022) An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design; 10.48550/arXiv.2210.15765
• Ma, Runzhuo and 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
• Su, Dan and Patwary, Mostofa, et el. (2022) Context Generation Improves Open Domain Question Answering; 10.48550/arXiv.2210.06349
• Qiao, Zhuoran and Nie, Weili, et el. (2022) Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models; 10.48550/arXiv.2209.15171
• Jeong, Yoonwoo and Shin, Seungjoo, et el. (2022) PeRFception: Perception using Radiance Fields; 10.48550/arXiv.2208.11537
• Cao, Yulong and Xu, Danfei, et el. (2022) Robust Trajectory Prediction against Adversarial Attacks; 10.48550/arXiv.2208.00094
• Cao, Yulong and Xiao, Chaowei, et el. (2022) AdvDO: Realistic Adversarial Attacks for Trajectory Prediction; 10.48550/arXiv.2209.08744
• Kocielnik, Rafal and Kangaslahti, Sara, et el. (2022) Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions; 10.48550/arXiv.2211.11798
• Xiao, Chaowei and Chen, Zhongzhu, et el. (2022) DensePure: Understanding Diffusion Models towards Adversarial Robustness; 10.48550/arXiv.2211.00322
• Xiao, Junfei and Xu, Zhichao, et el. (2022) 1st Place Solution of The Robust Vision Challenge 2022 Semantic Segmentation Track; 10.48550/arXiv.2210.12852
• Jiang, Yunfan and Gupta, Agrim, et el. (2022) VIMA: General Robot Manipulation with Multimodal Prompts; 10.48550/arXiv.2210.03094
• Netrapalli, Praneeth and Niranjan, U N, et el. (2022) Non-convex Robust PCA; 10.48550/arXiv.1410.7660
• Kurth, Thorsten and Subramanian, Shashank, et el. (2022) FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators; 10.48550/arXiv.2208.05419
• Wen, Gege and Li, Zongyi, et el. (2022) Accelerating Carbon Capture and Storage Modeling using Fourier Neural Operators; 10.48550/arXiv.2210.17051
• Huang, De-An and Yu, Zhiding, et el. (2022) MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training; 10.48550/arXiv.2208.02245
• Zhao, Jiawei and George, Robert Joseph, et el. (2022) Incremental Fourier Neural Operator; 10.48550/arXiv.2211.15188
• Maust, Haydn and Li, Zongyi, et el. (2022) Fourier Continuation for Exact Derivative Computation in Physics-Informed Neural Operators; 10.48550/arXiv.2211.15960
• Zheng, Hongkai and Nie, Weili, et el. (2022) Fast Sampling of Diffusion Models via Operator Learning; 10.48550/arXiv.2211.13449
• Sharir, Or and Chan, Garnet Kin-Lic, et el. (2022) Towards Neural Variational Monte Carlo That Scales Linearly with System Size
• Shi, Yuanyuan and Li, Zongyi, et el. (2022) Machine Learning Accelerated PDE Backstepping Observers; 10.48550/arXiv.2211.15044
• Laca, Jasper A. and Kocielnik, Rafal, et el. (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
• Shi, Yuanyuan and Li, Zongyi, et el. (2022) Machine Learning Accelerated PDE Backstepping Observers; ISBN 978-1-6654-6761-2; 2022 IEEE 61st Conference on Decision and Control (CDC); 5423-5428; 10.1109/cdc51059.2022.9992759
• Zhao, Jiawei and 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
• Trifan, Anda and Gorgun, Defne, et el. (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, et el. (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
• Markarian, Nicholas and Kugener, Guillaume, et el. (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
• Pangal, Dhiraj J. and Kugener, Guillaume, et el. (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
• Patti, Taylor L. and Kossaifi, Jean, et el. (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., et el. (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
• Li, Zongyi and Huang, Daniel Zhengyu, et el. (2022) Fourier Neural Operator with Learned Deformations for PDEs on General Geometries; 10.48550/arXiv.arXiv.2207.05209
• Lale, Sahin and Shi, Yuanyuan, et el. (2022) KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems; 10.48550/arXiv.arXiv.2206.01704
• Bharadhwaj, Homanga and Huang, De-An, et el. (2022) Auditing AI models for Verified Deployment under Semantic Specifications; 10.48550/arXiv.2109.12456
• Huang, Kevin and Lale, Sahin, et el. (2022) CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning; 10.48550/arXiv.arXiv.2112.07746
• Lavin, Alexander and Zenil, Hector, et el. (2022) Simulation Intelligence: Towards a New Generation of Scientific Methods; 10.48550/arXiv.arXiv.2112.03235
• Fan, Linxi and Wang, Guanzhi, et el. (2022) MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge; 10.48550/arXiv.arXiv.2206.08853
• Prabhumoye, Shrimai and Kocielnik, Rafal, et el. (2022) Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases; 10.48550/arXiv.arXiv.2112.07868
• Li, Shuang and Puig, Xavier, et el. (2022) Pre-Trained Language Models for Interactive Decision-Making; 10.48550/arXiv.arXiv.2202.01771
• Wen, Gege and Li, Zongyi, et el. (2022) U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flow; 10.48550/arXiv.arXiv.2109.03697
• Li, Zhiqi and Wang, Wenhai, et el. (2022) Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers; 10.48550/arXiv.2109.03814
• Guibas, John and Mardani, Morteza, et el. (2022) Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers; 10.48550/arXiv.arXiv.2111.13587
• Wong, Josiah and Makoviychuk, Viktor, et el. (2022) OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation; 10.48550/arXiv.arXiv.2110.00704
• Shen, Bokui and Jiang, Zhenyu, et el. (2022) ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation; 10.48550/arXiv.arXiv.2203.06856
• Pathak, Jaideep and Subramanian, Shashank, et el. (2022) FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators; 10.48550/arXiv.arXiv.2202.11214
• Ma, Xiaojian and Nie, Weili, et el. (2022) RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning; 10.48550/arXiv.arXiv.2204.11167
• Yang, Haoyu and Li, Zongyi, et el. (2022) Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks; 10.48550/arXiv.arXiv.2203.08616
• Yang, Haoyu and Li, Zongyi, et el. (2022) Large Scale Mask Optimization Via Convolutional Fourier Neural Operator and Litho-Guided Self Training; 10.48550/arXiv.arXiv.2207.04056
• Kiyasseh, Dani and Ma, Runzhuo, et el. (2022) Quantification of Robotic Surgeries with Vision-Based Deep Learning; 10.48550/arXiv.arXiv.2205.03028
• Mahajan, Anuj and Samvelyan, Mikayel, et el. (2022) Reinforcement Learning in Factored Action Spaces using Tensor Decompositions; 10.48550/arXiv.arXiv.2110.14538
• Jiang, Huaizu and Ma, Xiaojian, et el. (2022) Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions; 10.48550/arXiv.arXiv.2205.13803
• Rahman, Md Ashiqur and Florez, Manuel A., et el. (2022) Generative Adversarial Neural Operators; 10.48550/arXiv.arXiv.2205.03017
• Yang, Haoyu and Li, Zongyi, et el. (2022) Generic lithography modeling with dual-band optics-inspired neural networks; ISBN 9781450391429; DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference; 973-978; 10.1145/3489517.3530580
• Xu, Pan and Zheng, Hongkai, et el. (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, et el. (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
• Li, Zhiqi and Wang, Wenhai, et el. (2022) Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers; ISBN 978-1-6654-6946-3; 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 1270-1279; 10.1109/cvpr52688.2022.00134
• Wang, Xinlong and Yu, Zhiding, et el. (2022) FreeSOLO: Learning to Segment Objects without Annotations; 10.1109/cvpr52688.2022.01378
• Jiang, Huaizu and Ma, Xiaojian, et el. (2022) Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions; 10.1109/cvpr52688.2022.01847
• Elezi, Ismail and Yu, Zhiding, et el. (2022) Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection; ISBN 978-1-6654-6946-3; 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 14472-14781; 10.1109/cvpr52688.2022.01409
• Kugener, Guillaume and Zhu, Yichao, et el. (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
• Shi, Yuanyuan and Qu, Guannan, et el. (2022) Stability Constrained Reinforcement Learning for Real-Time Voltage Control; 2022 American Control Conference (ACC); 2715-2721; 10.23919/acc53348.2022.9867476
• Wong, Josiah and Makoviychuk, Viktor, et el. (2022) OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation; 10.1109/icra46639.2022.9811967
• Pangal, Dhiraj J. and Kugener, Guillaume, et el. (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, et el. (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, et el. (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
• Anandkumar, Anima (2022) ScaDL 2022 Invited Talk 3: Million-x speedups through convergence of AI and HPC; ISBN 978-1-6654-9747-3; 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW); 1041; 10.1109/ipdpsw55747.2022.00168
• Wen, Gege and Li, Zongyi, et el. (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
• Roberts, Sidney I. and Cen, Steven Y., et el. (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
• Zhou, Daquan and Yu, Zhiding, et el. (2022) Understanding The Robustness in Vision Transformers; Proceedings of Machine Learning Research; Vol. 162; 27378-27394; 10.48550/arXiv.2204.12451
• Xie, Enze and Yu, Zhiding, et el. (2022) M²BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation; 10.48550/arXiv.arXiv.2207.05850
• Shi, Yuanyuan and Qu, Guannan, et el. (2022) Stability Constrained Reinforcement Learning for Real-Time Voltage Control; 10.48550/arXiv.2109.14854
• Kugener, Guillaume and Pangal, Dhiraj J., et el. (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
• Wang, Xinlong and Yu, Zhiding, et el. (2022) FreeSOLO: Learning to Segment Objects without Annotations; 10.48550/arXiv.arXiv.2202.12181
• Liu, Burigede and Kovachki, Nikola, et el. (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
• Lale, Sahin and Azizzadenesheli, Kamyar, et el. (2021) Model Learning Predictive Control in Nonlinear Dynamical Systems; ISBN 978-1-6654-3659-5; 2021 60th IEEE Conference on Decision and Control (CDC); 757-762; 10.1109/cdc45484.2021.9683670
• Yu, Zhiding and Huang, Rui, et el. (2021) Coupled Segmentation and Edge Learning via Dynamic Graph Propagation; ISBN 9781713845393; 35th Conference on Neural Information Processing Systems (NeurIPS 2021); 1-14
• Zhu, Chen and Ping, Wei, et el. (2021) Long-Short Transformer: Efficient Transformers for Language and Vision; ISBN 9781713845393; Thirty-fifth Conference on Neural Information Processing Systems 35th Conference on Neural Information Processing Systems (NeurIPS 2021); 1-14
• Sun, Jiachen and Cao, Yulong, et el. (2021) Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions; ISBN 9781713845393; 35th Conference on Neural Information Processing Systems (NeurIPS 2021); 1-15
• Xie, Enze and Wang, Wenhai, et el. (2021) SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers; ISBN 9781713845393; Advances in Neural Information Processing Systems 34 (NeurIPS 2021); 1-14
• Christensen, Anders S. and Sirumalla, Sai Krishna, et el. (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
• Ma, Jeffrey and Letcher, Alistair, et el. (2021) Polymatrix Competitive Gradient Descent; 10.48550/arXiv.arXiv.2111.08565
• Dommer, Abigail and Casalino, Lorenzo, et el. (2021) #COVIDisAirborne: AI-Enabled Multiscale Computational Microscopy of Delta SARS-CoV-2 in a Respiratory Aerosol; PMCID PMC8609898; 10.1101/2021.11.12.468428
• Lee, Youngwoon and Lim, Joseph J., et el. (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
• Li, Zongyi and Zheng, Hongkai, et el. (2021) Physics-Informed Neural Operator for Learning Partial Differential Equations; 10.48550/arXiv.arXiv.2111.03794
• Huang, Yujia and Zhang, Huan, et el. (2021) Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds; ISBN 9781713845393; Advances in Neural Information Processing Systems 34 (NeurIPS 2021); 22745-22757; 10.48550/arXiv.arXiv.2111.01395
• Wang, Haotao and Xiao, Chaowei, et el. (2021) AugMax: Adversarial Composition of Random Augmentations for Robust Training; 10.48550/arXiv.arXiv.2110.13771
• Zhao, Jiawei and Schäfer, Florian, et el. (2021) ZerO Initialization: Initializing Residual Networks with only Zeros and Ones; 10.48550/arXiv.arXiv.2110.12661
• Nie, Weili and Vahdat, Arash, et el. (2021) Controllable and Compositional Generation with Latent-Space Energy-Based Models; ISBN 9781713845393; Advances in Neural Information Processing Systems 34 (NeurIPS 2021); 15498-15512; 10.48550/arXiv.arXiv.2110.10873
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• Anandkumar, Animashree (2020) Role of HPC in next-generation AI; ISBN 9781665422925; 2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics (HiPC); xx; 10.1109/hipc50609.2020.00010
• Lale, Sahin and Azizzadenesheli, Kamyar, et el. (2020) Explore More and Improve Regret in Linear Quadratic Regulators; 10.48550/arXiv.2007.12291
• Prajapat, Manish and Azizzadenesheli, Kamyar, et el. (2020) Competitive Policy Optimization; 10.48550/arXiv.2006.10611
• Li, Zongyi and Kovachki, Nikola, et el. (2020) Fourier Neural Operator for Parametric Partial Differential Equations; 10.48550/arXiv.2010.08895
• Schäfer, Florian and Anandkumar, Anima, et el. (2020) Competitive Mirror Descent; 10.48550/arXiv.2006.10179
• Wang, Haoxuan and Liu, Anqi, et el. (2020) Distributionally Robust Learning for Unsupervised Domain Adaptation; 10.48550/arXiv.2010.05784
• Xu, Peng and Patwary, Mostofa, et el. (2020) MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models; 10.48550/arXiv.2010.00840
• Jiang, Chiyu Max and Esmaeilzadeh, Soheil, et el. (2020) MESHFREEFLOWNET: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework; ISBN 978-1-7281-9998-6; SC20: International Conference for High Performance Computing, Networking, Storage and Analysis; 1-15; 10.1109/SC41405.2020.00013
• Chu, Linda C. and Anandkumar, Animashree, et el. (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
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• Baldini, Francesca and Anandkumar, Animashree, et el. (2020) Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data; ISBN 9781538682661; 2020 American Control Conference (ACC); 2961-2966; 10.23919/ACC45564.2020.9147400
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• Lale, Sahin and Azizzadenesheli, Kamyar, et el. (2020) Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems; 10.48550/arXiv.2003.11227
• Shi, Yang and Anandkumar, Animashree (2020) Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations; ISBN 978-1-7281-6457-1; 2020 Data Compression Conference (DCC); 394; 10.1109/DCC47342.2020.00045
• Lale, Sahin and Azizzadenesheli, Kamyar, et el. (2020) Regret Minimization in Partially Observable Linear Quadratic Control; 10.48550/arXiv.2002.00082
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• Schäfer, Florian and Zheng, Hongkai, et el. (2020) Implicit competitive regularization in GANs; 10.48550/arXiv.1910.05852
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• Schäfer, Florian and Anandkumar, Anima (2019) Competitive Gradient Descent; 10.48550/arXiv.1905.12103
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• 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
• Janzamin, Majid and Sedghi, Hanie, et el. (2019) Score Function Features for Discriminative Learning: Matrix and Tensor Framework; 10.48550/arXiv.1412.2863
• Shi, Yang and Furlanello, Tommaso, et el. (2019) Compact Tensor Pooling for Visual Question Answering; 10.48550/arXiv.1706.06706
• Anandkumar, Anima and Sedghi, Hanie (2019) Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods; 10.48550/arXiv.1503.04567
• Janzamin, Majid and Sedghi, Hanie, et el. (2019) Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods; 10.48550/arXiv.1506.08473
• Anandkumar, Animashree and Jain, Prateek, et el. (2019) Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations; 10.48550/arXiv.1510.04747
• Gitter, Anthony and Huang, Furong, et el. (2019) Unsupervised learning of transcriptional regulatory networks via latent tree graphical models; 10.48550/arXiv.1609.06335
• Huang, Furong and Anandkumar, Animashree (2019) Convolutional Dictionary Learning through Tensor Factorization; 10.48550/arXiv.1506.03509
• Sedghi, Hanie and Anandkumar, Anima (2019) Training Input-Output Recurrent Neural Networks through Spectral Methods; 10.48550/arXiv.1603.00954
• 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
• Ho, Nhat and Nguyen, Tan, et el. (2019) Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning; 10.48550/arXiv.1811.02657
• Azizzadenesheli, Kamyar and Liu, Anqi, et el. (2019) Regularized Learning for Domain Adaptation under Label Shifts; 10.48550/arXiv.1903.09734
• Kolbeinsson, Arinbjörn and Kossaifi, Jean, et el. (2019) Stochastically Rank-Regularized Tensor Regression Networks; 10.48550/arXiv.1902.10758
• Azizzadenesheli, Kamyar and Lazaric, Alessandro, et el. (2019) Experimental results: Reinforcement Learning of POMDPs using Spectral Methods; ISBN 9781510838819; 30th Annual Conference on Neural Information Processing Systems 2016 : Barcelona, Spain, 5-10 December 2016; 10.48550/arXiv.1705.02553
• Lale, Sahin and Azizzadenesheli, Kamyar, et el. (2019) Stochastic Linear Bandits with Hidden Low Rank Structure; 10.48550/arXiv.1901.09490
• Bernstein, Jeremy and Zhao, Jiawei, et el. (2019) signSGD with Majority Vote is Communication Efficient And Fault Tolerant; 10.48550/arXiv.1810.05291
• Azizzadenesheli, Kamyar and Lazaric, Alessandro, et el. (2019) Reinforcement Learning in Rich-Observation MDPs using Spectral Methods; 10.48550/arXiv.1611.03907v4
• Azizzadenesheli, Kamyar and Bera, Manish Kumar, et el. (2019) Trust Region Policy Optimization for POMDPs; 10.48550/arXiv.1810.07900
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• Kossaifi, Jean and Panagakis, Yannis, et el. (2019) TensorLy: Tensor Learning in Python; Journal of Machine Learning Research; Vol. 20; No. 26; 1-6; 10.48550/arXiv.1610.09555
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• Furlanello, Tommaso and Lipton, Zachary C., et el. (2018) Born Again Neural Networks; Proceedings of Machine Learning Research; Vol. 80; 1607-1616; 10.48550/arXiv.1805.04770
• Tschannen, Michael and Khanna, Aran, et el. (2018) StrassenNets: Deep Learning with a Multiplication Budget; Proceedings of Machine Learning Research; Vol. 80; 4985-4994; 10.48550/arXiv.1712.03942
• Athiwaratkun, Ben and Wilson, Andrew Gordon, et el. (2018) Probabilistic FastText for Multi-Sense Word Embeddings; ISBN 978-1-948087-32-2; Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers); Art. No. P18-1001; 10.48550/arXiv.1806.02901
• Arabshahi, Forough and Singh, Sameer, et el. (2018) Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs; 10.48550/arXiv.1801.04342
• Dhillon, Guneet S. and Azizzadenesheli, Kamyar, et el. (2018) Stochastic Activation Pruning for Robust Adversarial Defense; 10.48550/arXiv.1803.01442
• Khetan, Ashish and Lipton, Zachary C., et el. (2018) Learning From Noisy Singly-labeled Data; 10.48550/arXiv.1712.04577
• Shi, Yang and Furlanello, Tommaso, et el. (2018) Question Type Guided Attention in Visual Question Answering; ISBN 978-3-030-01224-3; Computer Vision – ECCV 2018; 158-175; 10.1007/978-3-030-01225-0_10
• Shen, Yanyao and Yun, Hyokun, et el. (2018) Deep Active Learning for Named Entity Recognition; 10.48550/arXiv.1707.05928
• Azizzadenesheli, Kamyar and Brunskill, Emma, et el. (2018) Efficient Exploration Through Bayesian Deep Q-Networks; ISBN 9781728101248; 2018 Information Theory and Applications Workshop (ITA); 1-9; 10.1109/ita.2018.8503252
• Anandkumar, Anima and Deng, Yuan, et el. (2017) Homotopy Analysis for Tensor PCA; Proceedings of Machine Learning Research; Vol. 65; 79-104; 10.48550/arXiv.1610.09322
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• Agarwal, Alekh and 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 and 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 and 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
• Shi, Yang and 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; 193-202; 10.1109/HiPC.2016.031
• Wang, Yining and Anandkumar, Animashree (2016) Online and Differentially-Private Tensor Decomposition; ISBN 9781510838819; Neural Information Processing Systems 2016; Art. No. 6498; 10.48550/arXiv.1606.06237
• Azizzadenesheli, Kamyar and 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
• Azizzadenesheli, Kamyar and Lazaric, Alessandro, et el. (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
• Anandkumar, Animashree and 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
• Huang, Furong and 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
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• Arabshahi, Forough and 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; 697-702; 10.1109/ICDM.2015.146
• Anandkumar, Animashree and Ge, Rong, et el. (2015) Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT); ISBN 978-3-319-24485-3; Algorithmic Learning Theory; 19-38; 10.1007/978-3-319-24486-0_2
• Janzamin, Majid and Sedghi, Hanie, et el. (2015) Score Function Features for Discriminative Learning; 10.48550/arXiv.1412.6514
• Anandkumar, Animashree and 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
• Sedghi, Hanie and Anandkumar, Anima (2014) Provable Methods for Training Neural Networks with Sparse Connectivity; 10.48550/arXiv.1412.2693
• Sedghi, Hanie and 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
• Netrapalli, Praneeth and 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; 1-9
• Sedghi, Hanie and 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; 1-9
• Anandkumar, Animashree and 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 and 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 and 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 and 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 and 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. and 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; 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; 1896-1904; 10.1109/INFCOM.2013.6566989
• Sattari, Pegah and 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; 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 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; 1-9
• Anandkumar, Anima and Foster, Dean P., et el. (2012) A Spectral Algorithm for Latent Dirichlet Allocation
• Anandkumar, Animashree and 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
• Liu, Ying and 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 and 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, Animashree and 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; 1-9
• Anandkumar, Animashree and 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; 1-9
• Anandkumar, Anima and 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; 1-9
• Anandkumar, Amod J. G. and 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 and Chaudhuri, Kamalika, et el. (2011) Spectral Methods for Learning Multivariate Latent Tree Structure; 10.48550/arXiv.1107.1283
• Khajehnejad, M. Amin and 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; 1427-1431; 10.1109/ISIT.2011.6033775
• Tan, Vincent Y. F. and 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 and 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
• Balister, Paul and 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; 1575-1583; 10.1109/INFCOM.2011.5934949
• He, Ting and 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; 1233-1241; 10.1109/INFCOM.2011.5934904
• Anandkumar, Animashree and 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
• Tan, Vincent Y. F. and 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. and 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; 482-486; 10.1109/ACSSC.2010.5757605
• Anandkumar, Animashree and 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; 1728-1732; 10.1109/ISIT.2010.5513254
• Liu, Ying and 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); 1683-1687; 10.1109/ISIT.2010.5513321
• Tan, Vincent Y. F. and 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; 1613-1617; 10.1109/ISIT.2010.5513399
• Tan, Vincent Y. F. and 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, Amod J. G. and 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; 3158-3161; 10.1109/ICASSP.2010.5496066
• Anandkumar, Animashree and Michael, Nithin, et el. (2010) Opportunistic Spectrum Access with Multiple Users: Learning under Competition; ISBN 978-1-4244-5836-3; 2010 Proceedings IEEE INFOCOM; 1-9; 10.1109/INFCOM.2010.5462144
• Tan, Vincent Y. F. and 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; 684-691; 10.1109/ALLERTON.2009.5394929
• Anandkumar, Animashree and 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. and 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; 1140-1144; 10.1109/ISIT.2009.5206012
• Anandkumar, Animashree and 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; 2882-2886; 10.1109/ISIT.2009.5205358
• Anandkumar, Animashree and 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; 2150-2158; 10.1109/INFCOM.2009.5062139
• Anandkumar, Animashree and 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 and 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 and 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; 1487-1494; 10.1109/ALLERTON.2008.4797738
• Anandkumar, Animashree and 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 and 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; 133-144; 10.1145/1375457.1375473
• Sengupta, Bikram and 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; 879-882; 10.1109/NOMS.2008.4575237
• Anandkumar, Animashree and 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; 1454-1462; 10.1109/INFOCOM.2008.129
• 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 and 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; 829-832; 10.1109/ICASSP.2007.366808
• Anandkumar, Animashree and 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.; 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; 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; 38-43; 10.1109/CISS.2006.286427