Anandkumar, Animashree

Book Chapter from CaltechAUTHORS

• 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
• Wang, Xinlong and Yu, Zhiding, et el. (2022) FreeSOLO: Learning to Segment Objects without Annotations; 10.1109/cvpr52688.2022.01378
• 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
• 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
• Jiang, Huaizu and Ma, Xiaojian, et el. (2022) Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions; 10.1109/cvpr52688.2022.01847
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Jeong, Yoonwoo and Ahn, Seokjun, et el. (2021) Self-Calibrating Neural Radiance Fields; ISBN 978-1-6654-2812-5; 2021 IEEE/CVF International Conference on Computer Vision (ICCV); 5826-5834; 10.1109/ICCV48922.2021.00579
• Lan, Shiyi and Yu, Zhiding, et el. (2021) DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision; ISBN 978-1-6654-2812-5; 2021 IEEE/CVF International Conference on Computer Vision (ICCV); 3386-3396; 10.1109/ICCV48922.2021.00339
• Srikanth, Maya and Liu, Anqi, et el. (2021) Dynamic Social Media Monitoring for Fast-Evolving Online Discussions; ISBN 978-1-4503-8332-5; Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining; 3576-3584; 10.1145/3447548.3467171
• Chrysos, Grigorios G. and Kossaifi, Jean, et el. (2021) Unsupervised Controllable Generation with Self-Training; ISBN 978-1-6654-3900-8; 2021 International Joint Conference on Neural Networks (IJCNN); 1-8; 10.1109/IJCNN52387.2021.9534045
• Pan, Xinlei and Garg, Animesh, et el. (2021) Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects; ISBN 978-1-7281-9077-8; 2021 IEEE International Conference on Robotics and Automation (ICRA); 7540-7547; 10.1109/ICRA48506.2021.9562092
• Shi, Guanya and Zhu, Yifeng, et el. (2021) Fast Uncertainty Quantification for Deep Object Pose Estimation; ISBN 978-1-7281-9077-8; 2021 IEEE International Conference on Robotics and Automation (ICRA); 5200-5207; 10.1109/ICRA48506.2021.9561483
• Ravi Tej, Akella and Azizzadenesheli, Kamyar, et el. (2021) Deep Bayesian Quadrature Policy Optimization; 10.48550/arXiv.2006.15637
• Lale, Sahin and Azizzadenesheli, Kamyar, et el. (2021) Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting; ISBN 978-1-6654-4197-1; 2021 American Control Conference (ACC); 2517-2522; 10.23919/ACC50511.2021.9483309
• Qiao, Zhuoran and Ding, Feizhi, et el. (2020) Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces; 10.48550/arXiv.2011.02680
• Lale, Sahin and Azizzadenesheli, Kamyar, et el. (2020) Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems; ISBN 9781713829546; Advances in neural information processing systems 33 pre-proceedings (NeurIPS 2020); 1-13
• Li, Zongyi and Kovachki, Nikola, et el. (2020) Multipole Graph Neural Operator for Parametric Partial Differential Equations; 10.48550/arXiv.2006.09535
• Su, Jiahao and Byeon, Wonmin, et el. (2020) Convolutional Tensor-Train LSTM for Spatio-temporal Learning; 10.48550/arXiv.2002.09131
• Bernstein, Jeremy and Zhao, Jiawei, et el. (2020) Learning compositional functions via multiplicative weight updates; 10.48550/arXiv.2006.14560
• Nie, Weili and Yu, Zhiding, et el. (2020) Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning; 10.48550/arXiv.2010.00763
• Li, Yunzhu and Torralba, Antonio, et el. (2020) Causal Discovery in Physical Systems from Videos; 10.48550/arXiv.2007.00631
• Huang, Yujia and Gornet, James, et el. (2020) Neural Networks with Recurrent Generative Feedback; 10.48550/arXiv.2007.09200
• 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
• 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
• 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
• 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
• Schäfer, Florian and Anandkumar, Anima (2019) Competitive Gradient Descent; 10.48550/arXiv.1905.12103
• Shi, Guanya and Shi, Xichen, et el. (2019) Neural Lander: Stable Drone Landing Control using Learned Dynamics; ISBN 978-1-5386-6027-0; 2019 International Conference on Robotics and Automation (ICRA); 9784-9790; 10.1109/ICRA.2019.8794351
• 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
• 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
• 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
• Kossaifi, Jean and Khanna, Aran, et el. (2017) Tensor Contraction Layers for Parsimonious Deep Nets; ISBN 978-1-5386-0733-6; 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 1940-1946; 10.1109/CVPRW.2017.243
• 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
• 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 Tung, Hsiao-Yu, et el. (2015) Fast and Guaranteed Tensor Decomposition via Sketching; 10.48550/arXiv.1506.04448
• 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
• 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
• 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
• 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, Anima and 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; 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, 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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 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
• 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
• 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. (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