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ORCIDAnima Anandkumar
Animashree Anandkumar
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- affiliation: University of California Irvine, Center for Pervasive Communications and Computing
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2020 – today
- 2021
[j31]Yashwanth Kumar Nakka
, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung:
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems. IEEE Robotics Autom. Lett. 6(1): 389-396 (2021)- [i128]Anqi Liu, Hao Liu, Tongxin Li, Saeed Karimi-Bidhendi, Yisong Yue, Anima Anandkumar:
Disentangling Observed Causal Effects from Latent Confounders using Method of Moments. CoRR abs/2101.06614 (2021) - [i127]Maya Srikanth, Anqi Liu, Nicholas Adams-Cohen, Jian Cao, R. Michael Alvarez, Anima Anandkumar:
Dynamic Social Media Monitoring for Fast-Evolving Online Discussions. CoRR abs/2102.12596 (2021) - 2020
- [j30]Jean Kossaifi, Zachary C. Lipton, Arinbjörn Kolbeinsson, Aran Khanna, Tommaso Furlanello, Anima Anandkumar:
Tensor Regression Networks. J. Mach. Learn. Res. 21: 123:1-123:21 (2020) - [c91]Francesca Baldini, Animashree Anandkumar, Richard M. Murray:
Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data. ACC 2020: 2961-2966 - [c90]Yang Shi, Animashree Anandkumar:
Higher-Order Count Sketch: Dimensionality Reduction that Retains Efficient Tensor Operations. DCC 2020: 394 - [c89]Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, Bryan Catanzaro:
MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models. EMNLP (1) 2020: 2831-2845 - [c88]Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar:
Angular Visual Hardness. ICML 2020: 1637-1648 - [c87]Wuyang Chen, Zhiding Yu, Zhangyang Wang, Animashree Anandkumar:
Automated Synthetic-to-Real Generalization. ICML 2020: 1746-1756 - [c86]Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B. Patel, Animashree Anandkumar:
Semi-Supervised StyleGAN for Disentanglement Learning. ICML 2020: 7360-7369 - [c85]Florian Schäfer, Hongkai Zheng, Animashree Anandkumar:
Implicit competitive regularization in GANs. ICML 2020: 8533-8544 - [c84]Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue:
Robust Regression for Safe Exploration in Control. L4DC 2020: 608-619 - [c83]Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue:
Learning compositional functions via multiplicative weight updates. NeurIPS 2020 - [c82]Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar:
Neural Networks with Recurrent Generative Feedback. NeurIPS 2020 - [c81]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems. NeurIPS 2020 - [c80]Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg:
Causal Discovery in Physical Systems from Videos. NeurIPS 2020 - [c79]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew M. Stuart, Kaushik Bhattacharya, Anima Anandkumar:
Multipole Graph Neural Operator for Parametric Partial Differential Equations. NeurIPS 2020 - [c78]Weili Nie, Zhiding Yu, Lei Mao, Ankit B. Patel, Yuke Zhu, Anima Anandkumar:
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning. NeurIPS 2020 - [c77]Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, Anima Anandkumar:
Convolutional Tensor-Train LSTM for Spatio-Temporal Learning. NeurIPS 2020 - [c76]Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar:
MeshfreeFlowNet: a physics-constrained deep continuous space-time super-resolution framework. SC 2020: 9 - [c75]Hongyu Ren, Yuke Zhu, Jure Leskovec, Animashree Anandkumar, Animesh Garg:
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation. UAI 2020: 1378-1387 - [i126]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Regret Minimization in Partially Observable Linear Quadratic Control. CoRR abs/2002.00082 (2020) - [i125]Jiahao Su, Wonmin Byeon, Furong Huang, Jan Kautz, Animashree Anandkumar:
Convolutional Tensor-Train LSTM for Spatio-temporal Learning. CoRR abs/2002.09131 (2020) - [i124]Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B. Patel, Anima Anandkumar:
Semi-Supervised StyleGAN for Disentanglement Learning. CoRR abs/2003.03461 (2020) - [i123]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Neural Operator: Graph Kernel Network for Partial Differential Equations. CoRR abs/2003.03485 (2020) - [i122]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Regret Bound of Adaptive Control in Linear Quadratic Gaussian (LQG) Systems. CoRR abs/2003.05999 (2020) - [i121]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems. CoRR abs/2003.11227 (2020) - [i120]Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar:
Spectral Learning on Matrices and Tensors. CoRR abs/2004.07984 (2020) - [i119]Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar:
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework. CoRR abs/2005.01463 (2020) - [i118]Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung:
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems. CoRR abs/2005.04374 (2020) - [i117]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Multipole Graph Neural Operator for Parametric Partial Differential Equations. CoRR abs/2006.09535 (2020) - [i116]Florian Schäfer, Anima Anandkumar, Houman Owhadi:
Competitive Mirror Descent. CoRR abs/2006.10179 (2020) - [i115]Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar:
Competitive Policy Optimization. CoRR abs/2006.10611 (2020) - [i114]Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue:
Learning compositional functions via multiplicative weight updates. CoRR abs/2006.14560 (2020) - [i113]Ravi Tej Akella, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Anima Anandkumar, Yisong Yue:
Deep Bayesian Quadrature Policy Optimization. CoRR abs/2006.15637 (2020) - [i112]Yunzhu Li, Antonio Torralba, Animashree Anandkumar, Dieter Fox, Animesh Garg:
Causal Discovery in Physical Systems from Videos. CoRR abs/2007.00631 (2020) - [i111]Wuyang Chen, Zhiding Yu, Zhangyang Wang, Anima Anandkumar:
Automated Synthetic-to-Real Generalization. CoRR abs/2007.06965 (2020) - [i110]Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller III:
OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features. CoRR abs/2007.08026 (2020) - [i109]Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue:
Active Learning under Label Shift. CoRR abs/2007.08479 (2020) - [i108]Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar:
Neural Networks with Recurrent Generative Feedback. CoRR abs/2007.09200 (2020) - [i107]Grigorios G. Chrysos, Jean Kossaifi, Zhiding Yu, Anima Anandkumar:
Unsupervised Controllable Generation with Self-Training. CoRR abs/2007.09250 (2020) - [i106]Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar:
Explore More and Improve Regret in Linear Quadratic Regulators. CoRR abs/2007.12291 (2020) - [i105]Hongyu Ren, Yuke Zhu, Jure Leskovec, Anima Anandkumar, Animesh Garg:
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation. CoRR abs/2008.07087 (2020) - [i104]Francisco Luongo, Ryan Hakim, Jessica H. Nguyen, Animashree Anandkumar, Andrew J. Hung:
Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery. CoRR abs/2008.11833 (2020) - [i103]Xingye Da, Zhaoming Xie, David Hoeller, Byron Boots, Animashree Anandkumar, Yuke Zhu, Buck Babich, Animesh Garg:
Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion. CoRR abs/2009.10019 (2020) - [i102]Weili Nie, Zhiding Yu, Lei Mao, Ankit B. Patel, Yuke Zhu, Animashree Anandkumar:
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning. CoRR abs/2010.00763 (2020) - [i101]Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, Bryan Catanzaro:
MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models. CoRR abs/2010.00840 (2020) - [i100]Haoxuan Wang, Anqi Liu, Zhiding Yu, Yisong Yue, Anima Anandkumar:
Distributionally Robust Learning for Unsupervised Domain Adaptation. CoRR abs/2010.05784 (2020) - [i99]Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar:
Fourier Neural Operator for Parametric Partial Differential Equations. CoRR abs/2010.08895 (2020) - [i98]Zhuoran Qiao, Feizhi Ding, Matthew Welborn, Peter J. Bygrave, Daniel G. A. Smith, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller III:
Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces. CoRR abs/2011.02680 (2020) - [i97]Guanya Shi, Yifeng Zhu, Jonathan Tremblay, Stan Birchfield, Fabio Ramos, Animashree Anandkumar, Yuke Zhu:
Fast Uncertainty Quantification for Deep Object Pose Estimation. CoRR abs/2011.07748 (2020) - [i96]Sahin Lale, Oguzhan Teke, Babak Hassibi, Anima Anandkumar:
Stability and Identification of Random Asynchronous Linear Time-Invariant Systems. CoRR abs/2012.04160 (2020) - [i95]Xinlei Pan, Animesh Garg, Animashree Anandkumar, Yuke Zhu:
Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects. CoRR abs/2012.12209 (2020)
2010 – 2019
- 2019
- [j29]Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar:
Spectral Learning on Matrices and Tensors. Found. Trends Mach. Learn. 12(5-6): 393-536 (2019) - [j28]Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic:
TensorLy: Tensor Learning in Python. J. Mach. Learn. Res. 20: 26:1-26:6 (2019) - [c74]Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, Animashree Anandkumar:
Regularized Learning for Domain Adaptation under Label Shifts. ICLR (Poster) 2019 - [c73]Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, Anima Anandkumar:
signSGD with Majority Vote is Communication Efficient and Fault Tolerant. ICLR (Poster) 2019 - [c72]Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan:
Active Learning with Partial Feedback. ICLR (Poster) 2019 - [c71]Milan Cvitkovic, Badal Singh, Animashree Anandkumar:
Open Vocabulary Learning on Source Code with a Graph-Structured Cache. ICML 2019: 1475-1485 - [c70]Shobhit Jain, Sravan Babu Bodapati, Ramesh Nallapati, Anima Anandkumar:
Multi Sense Embeddings from Topic Models. ICNLSP 2019: 34-41 - [c69]Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung:
Neural Lander: Stable Drone Landing Control Using Learned Dynamics. ICRA 2019: 9784-9790 - [c68]Florian Schäfer, Anima Anandkumar:
Competitive Gradient Descent. NeurIPS 2019: 7623-7633 - [c67]Furong Huang, U. N. Niranjan, Ioakeim Perros, Robert Chen, Jimeng Sun, Anima Anandkumar:
Guaranteed Scalable Learning of Latent Tree Models. UAI 2019: 883-893 - [i94]Sahin Lale, Kamyar Azizzadenesheli, Anima Anandkumar, Babak Hassibi:
Stochastic Linear Bandits with Hidden Low Rank Structure. CoRR abs/1901.09490 (2019) - [i93]Yang Shi, Animashree Anandkumar:
Multi-dimensional Tensor Sketch. CoRR abs/1901.11261 (2019) - [i92]Arinbjörn Kolbeinsson, Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Ioanna Tzoulaki, Paul Matthews:
Stochastically Rank-Regularized Tensor Regression Networks. CoRR abs/1902.10758 (2019) - [i91]Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, Animashree Anandkumar:
Regularized Learning for Domain Adaptation under Label Shifts. CoRR abs/1903.09734 (2019) - [i90]Florian Schäfer, Anima Anandkumar:
Competitive Gradient Descent. CoRR abs/1905.12103 (2019) - [i89]Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue:
Robust Regression for Safe Exploration in Control. CoRR abs/1906.05819 (2019) - [i88]Amy Zhang, Zachary C. Lipton, Luis Pineda, Kamyar Azizzadenesheli, Anima Anandkumar, Laurent Itti, Joelle Pineau, Tommaso Furlanello:
Learning Causal State Representations of Partially Observable Environments. CoRR abs/1906.10437 (2019) - [i87]Zachary E. Ross, Daniel T. Trugman, Kamyar Azizzadenesheli, Anima Anandkumar:
Directivity Modes of Earthquake Populations with Unsupervised Learning. CoRR abs/1907.00496 (2019) - [i86]Yujia Huang, Sihui Dai, Tan M. Nguyen, Richard G. Baraniuk, Anima Anandkumar:
Out-of-Distribution Detection Using Neural Rendering Generative Models. CoRR abs/1907.04572 (2019) - [i85]Shobhit Jain, Sravan Babu Bodapati, Ramesh Nallapati, Anima Anandkumar:
Multi Sense Embeddings from Topic Models. CoRR abs/1909.07746 (2019) - [i84]Florian Schäfer, Hongkai Zheng, Anima Anandkumar:
Implicit competitive regularization in GANs. CoRR abs/1910.05852 (2019) - [i83]Forough Arabshahi, Zhichu Lu, Sameer Singh, Animashree Anandkumar:
Memory Augmented Recursive Neural Networks. CoRR abs/1911.01545 (2019) - [i82]Anqi Liu, Maya Srikanth, Nicholas Adams-Cohen, R. Michael Alvarez, Anima Anandkumar:
Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates. CoRR abs/1911.05332 (2019) - [i81]Anqi Liu, Hao Liu, Anima Anandkumar, Yisong Yue:
Triply Robust Off-Policy Evaluation. CoRR abs/1911.05811 (2019) - [i80]Beidi Chen, Weiyang Liu, Animesh Garg, Zhiding Yu, Anshumali Shrivastava, Jan Kautz, Anima Anandkumar:
Angular Visual Hardness. CoRR abs/1912.02279 (2019) - [i79]Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk, Anima Anandkumar:
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers. CoRR abs/1912.03978 (2019) - [i78]Francesca Baldini, Animashree Anandkumar, Richard M. Murray:
Learning Pose Estimation for UAV Autonomous Navigation andLanding Using Visual-Inertial Sensor Data. CoRR abs/1912.04527 (2019) - 2018
- [j27]Evrim Acar, Animashree Anandkumar, Lenore Mullin, Sebnem Rusitschka, Volker Tresp:
Tensor Computing for Internet of Things (Dagstuhl Perspectives Workshop 16152). Dagstuhl Manifestos 7(1): 52-68 (2018) - [c66]Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar:
Probabilistic FastText for Multi-Sense Word Embeddings. ACL (1) 2018: 1-11 - [c65]Yang Shi, Tommaso Furlanello, Sheng Zha, Animashree Anandkumar:
Question Type Guided Attention in Visual Question Answering. ECCV (4) 2018: 158-175 - [c64]Forough Arabshahi, Sameer Singh, Animashree Anandkumar:
Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs. ICLR (Poster) 2018 - [c63]Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar:
Compression by the signs: distributed learning is a two-way street. ICLR (Workshop) 2018 - [c62]Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Animashree Anandkumar:
Stochastic Activation Pruning for Robust Adversarial Defense. ICLR (Poster) 2018 - [c61]Ashish Khetan, Zachary C. Lipton, Animashree Anandkumar:
Learning From Noisy Singly-labeled Data. ICLR (Poster) 2018 - [c60]Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Animashree Anandkumar:
Deep Active Learning for Named Entity Recognition. ICLR (Poster) 2018 - [c59]Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar:
SIGNSGD: Compressed Optimisation for Non-Convex Problems. ICML 2018: 559-568 - [c58]Tommaso Furlanello, Zachary Chase Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar:
Born-Again Neural Networks. ICML 2018: 1602-1611 - [c57]Michael Tschannen, Aran Khanna, Animashree Anandkumar:
StrassenNets: Deep Learning with a Multiplication Budget. ICML 2018: 4992-5001 - [c56]Kamyar Azizzadenesheli, Emma Brunskill, Animashree Anandkumar:
Efficient Exploration Through Bayesian Deep Q-Networks. ITA 2018: 1-9 - [i77]Forough Arabshahi, Sameer Singh, Animashree Anandkumar:
Combining Symbolic and Function Evaluation Expressions In Neural Programs. CoRR abs/1801.04342 (2018) - [i76]Kamyar Azizzadenesheli, Emma Brunskill, Animashree Anandkumar:
Efficient Exploration through Bayesian Deep Q-Networks. CoRR abs/1802.04412 (2018) - [i75]Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar:
signSGD: compressed optimisation for non-convex problems. CoRR abs/1802.04434 (2018) - [i74]Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan:
Active Learning with Partial Feedback. CoRR abs/1802.07427 (2018) - [i73]Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Anima Anandkumar:
Stochastic Activation Pruning for Robust Adversarial Defense. CoRR abs/1803.01442 (2018) - [i72]Yang Shi, Tommaso Furlanello, Sheng Zha, Animashree Anandkumar:
Question Type Guided Attention in Visual Question Answering. CoRR abs/1804.02088 (2018) - [i71]Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar:
Born Again Neural Networks. CoRR abs/1805.04770 (2018) - [i70]Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar:
Probabilistic FastText for Multi-Sense Word Embeddings. CoRR abs/1806.02901 (2018) - [i69]Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Emma Brunskill, Zachary C. Lipton, Animashree Anandkumar:
Sample-Efficient Deep RL with Generative Adversarial Tree Search. CoRR abs/1806.05780 (2018) - [i68]Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, Anima Anandkumar:
signSGD with Majority Vote is Communication Efficient And Byzantine Fault Tolerant. CoRR abs/1810.05291 (2018) - [i67]Kamyar Azizzadenesheli, Manish Kumar Bera, Animashree Anandkumar:
Trust Region Policy Optimization of POMDPs. CoRR abs/1810.07900 (2018) - [i66]Milan Cvitkovic, Badal Singh, Anima Anandkumar:
Open Vocabulary Learning on Source Code with a Graph-Structured Cache. CoRR abs/1810.08305 (2018) - [i65]Nhat Ho, Tan M. Nguyen, Ankit B. Patel, Anima Anandkumar, Michael I. Jordan, Richard G. Baraniuk:
Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning. CoRR abs/1811.02657 (2018) - [i64]Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung:
Neural Lander: Stable Drone Landing Control using Learned Dynamics. CoRR abs/1811.08027 (2018) - 2017
- [j26]Animashree Anandkumar, Rong Ge, Majid Janzamin:
Analyzing Tensor Power Method Dynamics in Overcomplete Regime. J. Mach. Learn. Res. 18: 22:1-22:40 (2017) - [j25]Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli:
A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries. IEEE Trans. Inf. Theory 63(1): 575-592 (2017) - [c55]Forough Arabshahi, Anima Anandkumar:
Spectral Methods for Correlated Topic Models. AISTATS 2017: 1439-1447 - [c54]Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi:
Homotopy Analysis for Tensor PCA. COLT 2017: 79-104 - [c53]Jean Kossaifi, Aran Khanna, Zachary Chase Lipton, Tommaso Furlanello, Anima Anandkumar:
Tensor Contraction Layers for Parsimonious Deep Nets. CVPR Workshops 2017: 1940-1946 - [c52]Yanyao Shen, Hyokun Yun, Zachary Chase Lipton, Yakov Kronrod, Animashree Anandkumar:
Deep Active Learning for Named Entity Recognition. Rep4NLP@ACL 2017: 252-256 - [i63]Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar:
Experimental results : Reinforcement Learning of POMDPs using Spectral Methods. CoRR abs/1705.02553 (2017) - [i62]Jean Kossaifi, Aran Khanna, Zachary C. Lipton, Tommaso Furlanello, Anima Anandkumar:
Tensor Contraction Layers for Parsimonious Deep Nets. CoRR abs/1706.00439 (2017) - [i61]Yang Shi, Tommaso Furlanello, Anima Anandkumar:
Compact Tensor Pooling for Visual Question Answering. CoRR abs/1706.06706 (2017) - [i60]Yanyao Shen, Hyokun Yun, Zachary C. Lipton, Yakov Kronrod, Animashree Anandkumar:
Deep Active Learning for Named Entity Recognition. CoRR abs/1707.05928 (2017) - [i59]Jean Kossaifi, Zachary C. Lipton, Aran Khanna, Tommaso Furlanello, Anima Anandkumar:
Tensor Regression Networks. CoRR abs/1707.08308 (2017) - [i58]Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue:
Long-term Forecasting using Tensor-Train RNNs. CoRR abs/1711.00073 (2017) - [i57]Michael Tschannen, Aran Khanna, Anima Anandkumar:
StrassenNets: Deep learning with a multiplication budget. CoRR abs/1712.03942 (2017) - [i56]Ashish Khetan, Zachary C. Lipton, Anima Anandkumar:
Learning From Noisy Singly-labeled Data. CoRR abs/1712.04577 (2017) - 2016
- [j24]Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli:
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. SIAM J. Optim. 26(4): 2775-2799 (2016) - [c51]Anima Anandkumar, Prateek Jain, Yang Shi, U. N. Niranjan:
Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations. AISTATS 2016: 268-276 - [c50]Hanie Sedghi, Majid Janzamin, Anima Anandkumar:
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models. AISTATS 2016: 1223-1231 - [c49]Animashree Anandkumar, Rong Ge:
Efficient approaches for escaping higher order saddle points in non-convex optimization. COLT 2016: 81-102 - [c48]Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar:
Reinforcement Learning of POMDPs using Spectral Methods. COLT 2016: 193-256 - [c47]Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar:
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies. COLT 2016: 1639-1642 - [c46]Yang Shi, U. N. Niranjan, Animashree Anandkumar, Cris Cecka:
Tensor Contractions with Extended BLAS Kernels on CPU and GPU. HiPC 2016: 193-202 - [c45]Yining Wang, Anima Anandkumar:
Online and Differentially-Private Tensor Decomposition. NIPS 2016: 3531-3539 - [i55]Anima Anandkumar, Rong Ge:
Efficient approaches for escaping higher order saddle points in non-convex optimization. CoRR abs/1602.05908 (2016) - [i54]Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar:
Reinforcement Learning of POMDP's using Spectral Methods. CoRR abs/1602.07764 (2016) - [i53]Hanie Sedghi, Anima Anandkumar:
Training Input-Output Recurrent Neural Networks through Spectral Methods. CoRR abs/1603.00954 (2016) - [i52]Forough Arabshahi, Animashree Anandkumar:
Beyond LDA: A Unified Framework for Learning Latent Normalized Infinitely Divisible Topic Models through Spectral Methods. CoRR abs/1605.09080 (2016) - [i51]