Anima Anandkumar

According to our database, Anima Anandkumar authored at least 186 papers between 2006 and 2021.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of two.

2021

Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems.

Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung

IEEE Robotics Autom. Lett., 2021

Tensor Dropout for Robust Learning.

Arinbjörn Kolbeinsson, Jean Kossaifi, Yannis Panagakis, Adrian Bulat, Animashree Anandkumar, Ioanna Tzoulaki, Paul M. Matthews

IEEE J. Sel. Top. Signal Process., 2021

Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection.

Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Anima Anandkumar, Sanja Fidler, Jose M. Alvarez

CoRR, 2021

Contrastive Syn-to-Real Generalization.

Wuyang Chen, Zhiding Yu, Shalini De Mello, Sifei Liu, Jose M. Alvarez, Zhangyang Wang, Anima Anandkumar

CoRR, 2021

Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles.

Zahra Ghodsi, Siva Kumar Sastry Hari, Iuri Frosio, Timothy Tsai, Alejandro Troccoli, Stephen W. Keckler, Siddharth Garg, Anima Anandkumar

CoRR, 2021

Dynamic Social Media Monitoring for Fast-Evolving Online Discussions.

Maya Srikanth, Anqi Liu, Nicholas Adams-Cohen, Jian Cao, R. Michael Alvarez, Anima Anandkumar

CoRR, 2021

Disentangling Observed Causal Effects from Latent Confounders using Method of Moments.

Anqi Liu, Hao Liu, Tongxin Li, Saeed Karimi-Bidhendi, Yisong Yue, Anima Anandkumar

CoRR, 2021

Active Learning under Label Shift.

Eric Zhao, Anqi Liu, Animashree Anandkumar, Yisong Yue

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Tensor Regression Networks.

Jean Kossaifi, Zachary C. Lipton, Arinbjörn Kolbeinsson, Aran Khanna, Tommaso Furlanello, Anima Anandkumar

J. Mach. Learn. Res., 2020

Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects.

Xinlei Pan, Animesh Garg, Animashree Anandkumar, Yuke Zhu

CoRR, 2020

Stability and Identification of Random Asynchronous Linear Time-Invariant Systems.

Sahin Lale, Oguzhan Teke, Babak Hassibi, Anima Anandkumar

CoRR, 2020

Fast Uncertainty Quantification for Deep Object Pose Estimation.

Guanya Shi, Yifeng Zhu, Jonathan Tremblay, Stan Birchfield, Fabio Ramos, Animashree Anandkumar, Yuke Zhu

CoRR, 2020

Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces.

Zhuoran Qiao, Feizhi Ding, Matthew Welborn, Peter J. Bygrave, Daniel G. A. Smith, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller III

CoRR, 2020

Fourier Neural Operator for Parametric Partial Differential Equations.

Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar

CoRR, 2020

Distributionally Robust Learning for Unsupervised Domain Adaptation.

Haoxuan Wang, Anqi Liu, Zhiding Yu, Yisong Yue, Anima Anandkumar

CoRR, 2020

Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion.

Xingye Da, Zhaoming Xie, David Hoeller, Byron Boots, Animashree Anandkumar, Yuke Zhu, Buck Babich, Animesh Garg

CoRR, 2020

Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery.

Francisco Luongo, Ryan Hakim, Jessica H. Nguyen, Animashree Anandkumar, Andrew J. Hung

CoRR, 2020

Explore More and Improve Regret in Linear Quadratic Regulators.

Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

CoRR, 2020

Unsupervised Controllable Generation with Self-Training.

Grigorios G. Chrysos, Jean Kossaifi, Zhiding Yu, Anima Anandkumar

CoRR, 2020

OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features.

Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller III

CoRR, 2020

Deep Bayesian Quadrature Policy Optimization.

Ravi Tej Akella, Kamyar Azizzadenesheli, Mohammad Ghavamzadeh, Anima Anandkumar, Yisong Yue

CoRR, 2020

Competitive Policy Optimization.

Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar

CoRR, 2020

Competitive Mirror Descent.

Florian Schäfer, Anima Anandkumar, Houman Owhadi

CoRR, 2020

Regret Bound of Adaptive Control in Linear Quadratic Gaussian (LQG) Systems.

Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

CoRR, 2020

Neural Operator: Graph Kernel Network for Partial Differential Equations.

Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar

CoRR, 2020

Convolutional Tensor-Train LSTM for Spatio-temporal Learning.

Jiahao Su, Wonmin Byeon, Furong Huang, Jan Kautz, Animashree Anandkumar

CoRR, 2020

Regret Minimization in Partially Observable Linear Quadratic Control.

Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

CoRR, 2020

OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation.

Hongyu Ren, Yuke Zhu, Jure Leskovec, Animashree Anandkumar, Animesh Garg

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

MeshfreeFlowNet: a physics-constrained deep continuous space-time super-resolution framework.

Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar

Proceedings of the International Conference for High Performance Computing, 2020

Convolutional Tensor-Train LSTM for Spatio-Temporal Learning.

Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, Anima Anandkumar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning.

Weili Nie, Zhiding Yu, Lei Mao, Ankit B. Patel, Yuke Zhu, Anima Anandkumar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Multipole Graph Neural Operator for Parametric Partial Differential Equations.

Zongyi Li, Nikola B. Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew M. Stuart, Kaushik Bhattacharya, Anima Anandkumar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Causal Discovery in Physical Systems from Videos.

Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems.

Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Neural Networks with Recurrent Generative Feedback.

Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning compositional functions via multiplicative weight updates.

Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Robust Regression for Safe Exploration in Control.

Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Implicit competitive regularization in GANs.

Florian Schäfer, Hongkai Zheng, Animashree Anandkumar

Proceedings of the 37th International Conference on Machine Learning, 2020

Semi-Supervised StyleGAN for Disentanglement Learning.

Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B. Patel, Animashree Anandkumar

Proceedings of the 37th International Conference on Machine Learning, 2020

Automated Synthetic-to-Real Generalization.

Wuyang Chen, Zhiding Yu, Zhangyang Wang, Animashree Anandkumar

Proceedings of the 37th International Conference on Machine Learning, 2020

Angular Visual Hardness.

Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar

Proceedings of the 37th International Conference on Machine Learning, 2020

MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models.

Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, Bryan Catanzaro

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

Higher-Order Count Sketch: Dimensionality Reduction that Retains Efficient Tensor Operations.

Yang Shi, Animashree Anandkumar

Proceedings of the Data Compression Conference, 2020

Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data.

Francesca Baldini, Animashree Anandkumar, Richard M. Murray

Proceedings of the 2020 American Control Conference, 2020

2019

TensorLy: Tensor Learning in Python.

Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic

J. Mach. Learn. Res., 2019

Spectral Learning on Matrices and Tensors.

Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar

Found. Trends Mach. Learn., 2019

Learning Pose Estimation for UAV Autonomous Navigation andLanding Using Visual-Inertial Sensor Data.

Francesca Baldini, Animashree Anandkumar, Richard M. Murray

CoRR, 2019

InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers.

Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk, Anima Anandkumar

CoRR, 2019

Triply Robust Off-Policy Evaluation.

Anqi Liu, Hao Liu, Anima Anandkumar, Yisong Yue

CoRR, 2019

Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates.

Anqi Liu, Maya Srikanth, Nicholas Adams-Cohen, R. Michael Alvarez, Anima Anandkumar

CoRR, 2019

Memory Augmented Recursive Neural Networks.

Forough Arabshahi, Zhichu Lu, Sameer Singh, Animashree Anandkumar

CoRR, 2019

Out-of-Distribution Detection Using Neural Rendering Generative Models.

Yujia Huang, Sihui Dai, Tan M. Nguyen, Richard G. Baraniuk, Anima Anandkumar

CoRR, 2019

Directivity Modes of Earthquake Populations with Unsupervised Learning.

Zachary E. Ross, Daniel T. Trugman, Kamyar Azizzadenesheli, Anima Anandkumar

CoRR, 2019

Learning Causal State Representations of Partially Observable Environments.

Amy Zhang, Zachary C. Lipton, Luis Pineda, Kamyar Azizzadenesheli, Anima Anandkumar, Laurent Itti, Joelle Pineau, Tommaso Furlanello

CoRR, 2019

Stochastically Rank-Regularized Tensor Regression Networks.

Arinbjörn Kolbeinsson, Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Ioanna Tzoulaki, Paul M. Matthews

CoRR, 2019

Multi-dimensional Tensor Sketch.

Yang Shi, Animashree Anandkumar

CoRR, 2019

Stochastic Linear Bandits with Hidden Low Rank Structure.

Sahin Lale, Kamyar Azizzadenesheli, Anima Anandkumar, Babak Hassibi

CoRR, 2019

Guaranteed Scalable Learning of Latent Tree Models.

Furong Huang, U. N. Niranjan, Ioakeim Perros, Robert Chen, Jimeng Sun, Anima Anandkumar

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Competitive Gradient Descent.

Florian Schäfer, Anima Anandkumar

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Neural Lander: Stable Drone Landing Control Using Learned Dynamics.

Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung

Proceedings of the International Conference on Robotics and Automation, 2019

Multi Sense Embeddings from Topic Models.

Shobhit Jain, Sravan Babu Bodapati, Ramesh Nallapati, Anima Anandkumar

Proceedings of the 3rd International Conference on Natural Language and Speech Processing, 2019

Open Vocabulary Learning on Source Code with a Graph-Structured Cache.

Milan Cvitkovic, Badal Singh, Animashree Anandkumar

Proceedings of the 36th International Conference on Machine Learning, 2019

Active Learning with Partial Feedback.

Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan

Proceedings of the 7th International Conference on Learning Representations, 2019

signSGD with Majority Vote is Communication Efficient and Fault Tolerant.

Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, Anima Anandkumar

Proceedings of the 7th International Conference on Learning Representations, 2019

Regularized Learning for Domain Adaptation under Label Shifts.

Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang, Animashree Anandkumar

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning.

Nhat Ho, Tan M. Nguyen, Ankit B. Patel, Anima Anandkumar, Michael I. Jordan, Richard G. Baraniuk

CoRR, 2018

Trust Region Policy Optimization of POMDPs.

Kamyar Azizzadenesheli, Manish Kumar Bera, Animashree Anandkumar

CoRR, 2018

signSGD with Majority Vote is Communication Efficient And Byzantine Fault Tolerant.

Jeremy Bernstein, Jiawei Zhao, Kamyar Azizzadenesheli, Anima Anandkumar

CoRR, 2018

Sample-Efficient Deep RL with Generative Adversarial Tree Search.

Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Emma Brunskill, Zachary C. Lipton, Animashree Anandkumar

CoRR, 2018

Combining Symbolic and Function Evaluation Expressions In Neural Programs.

Forough Arabshahi, Sameer Singh, Animashree Anandkumar

CoRR, 2018

Efficient Exploration Through Bayesian Deep Q-Networks.

Kamyar Azizzadenesheli, Emma Brunskill, Animashree Anandkumar

Proceedings of the 2018 Information Theory and Applications Workshop, 2018

StrassenNets: Deep Learning with a Multiplication Budget.

Michael Tschannen, Aran Khanna, Animashree Anandkumar

Proceedings of the 35th International Conference on Machine Learning, 2018

Born-Again Neural Networks.

Tommaso Furlanello, Zachary Chase Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar

Proceedings of the 35th International Conference on Machine Learning, 2018

SIGNSGD: Compressed Optimisation for Non-Convex Problems.

Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Animashree Anandkumar

Proceedings of the 35th International Conference on Machine Learning, 2018

Learning From Noisy Singly-labeled Data.

Ashish Khetan, Zachary C. Lipton, Animashree Anandkumar

Proceedings of the 6th International Conference on Learning Representations, 2018

Stochastic Activation Pruning for Robust Adversarial Defense.

Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Animashree Anandkumar

Proceedings of the 6th International Conference on Learning Representations, 2018

Compression by the signs: distributed learning is a two-way street.

Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar

Proceedings of the 6th International Conference on Learning Representations, 2018

Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs.

Forough Arabshahi, Sameer Singh, Animashree Anandkumar

Proceedings of the 6th International Conference on Learning Representations, 2018

Question Type Guided Attention in Visual Question Answering.

Yang Shi, Tommaso Furlanello, Sheng Zha, Animashree Anandkumar

Proceedings of the Computer Vision - ECCV 2018, 2018

Probabilistic FastText for Multi-Sense Word Embeddings.

Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018

2017

A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries.

Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli

IEEE Trans. Inf. Theory, 2017

Analyzing Tensor Power Method Dynamics in Overcomplete Regime.

Animashree Anandkumar, Rong Ge, Majid Janzamin

J. Mach. Learn. Res., 2017

Long-term Forecasting using Tensor-Train RNNs.

Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue

CoRR, 2017

Compact Tensor Pooling for Visual Question Answering.

Yang Shi, Tommaso Furlanello, Anima Anandkumar

CoRR, 2017

Tensor Regression Networks.

Jean Kossaifi, Zachary C. Lipton, Aran Khanna, Tommaso Furlanello, Anima Anandkumar

CoRR, 2017

Experimental results : Reinforcement Learning of POMDPs using Spectral Methods.

Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar

CoRR, 2017

Deep Active Learning for Named Entity Recognition.

Yanyao Shen, Hyokun Yun, Zachary Chase Lipton, Yakov Kronrod, Animashree Anandkumar

Proceedings of the 2nd Workshop on Representation Learning for NLP, 2017

Tensor Contraction Layers for Parsimonious Deep Nets.

Jean Kossaifi, Aran Khanna, Zachary Chase Lipton, Tommaso Furlanello, Anima Anandkumar

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017

Homotopy Analysis for Tensor PCA.

Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi

Proceedings of the 30th Conference on Learning Theory, 2017

Spectral Methods for Correlated Topic Models.

Forough Arabshahi, Anima Anandkumar

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization.

Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli

SIAM J. Optim., 2016

Tensor Computing for Internet of Things (Dagstuhl Perspectives Workshop 16152).

Evrim Acar, Animashree Anandkumar, Lenore Mullin, Sebnem Rusitschka, Volker Tresp

Dagstuhl Reports, 2016

Training Input-Output Recurrent Neural Networks through Spectral Methods.

Hanie Sedghi, Anima Anandkumar

CoRR, 2016

Unsupervised learning of transcriptional regulatory networks via latent tree graphical models.

Anthony Gitter, Furong Huang, Ragupathyraj Valluvan, Ernest Fraenkel, Animashree Anandkumar

CoRR, 2016

Reinforcement Learning of Contextual MDPs using Spectral Methods.

Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar

CoRR, 2016

Reinforcement Learning of POMDP's using Spectral Methods.

Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar

CoRR, 2016

Beyond LDA: A Unified Framework for Learning Latent Normalized Infinitely Divisible Topic Models through Spectral Methods.

Forough Arabshahi, Animashree Anandkumar

CoRR, 2016

Homotopy Method for Tensor Principal Component Analysis.

Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi

CoRR, 2016

Online and Differentially-Private Tensor Decomposition.

Yining Wang, Anima Anandkumar

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Tensor Contractions with Extended BLAS Kernels on CPU and GPU.

Yang Shi, U. N. Niranjan, Animashree Anandkumar, Cris Cecka

Proceedings of the 23rd IEEE International Conference on High Performance Computing, 2016

Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies.

Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar

Proceedings of the 29th Conference on Learning Theory, 2016

Reinforcement Learning of POMDPs using Spectral Methods.

Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar

Proceedings of the 29th Conference on Learning Theory, 2016

Efficient approaches for escaping higher order saddle points in non-convex optimization.

Animashree Anandkumar, Rong Ge

Proceedings of the 29th Conference on Learning Theory, 2016

Provable Tensor Methods for Learning Mixtures of Generalized Linear Models.

Hanie Sedghi, Majid Janzamin, Anima Anandkumar

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations.

Anima Anandkumar, Prateek Jain, Yang Shi, U. N. Niranjan

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Online tensor methods for learning latent variable models.

Furong Huang, U. N. Niranjan, Mohammad Umar Hakeem, Animashree Anandkumar

J. Mach. Learn. Res., 2015

When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity.

Animashree Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade

J. Mach. Learn. Res., 2015

Provable Methods for Training Neural Networks with Sparse Connectivity.

Hanie Sedghi, Anima Anandkumar

Proceedings of the 3rd International Conference on Learning Representations, 2015

Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models.

Tejaswi Nimmagadda, Anima Anandkumar

CoRR, 2015

Generalization Bounds for Neural Networks through Tensor Factorization.

Majid Janzamin, Hanie Sedghi, Anima Anandkumar

CoRR, 2015

Score Function Features for Discriminative Learning.

Majid Janzamin, Hanie Sedghi, Anima Anandkumar

Proceedings of the 3rd International Conference on Learning Representations, 2015

Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods.

Anima Anandkumar, Hanie Sedghi

CoRR, 2015

A Spectral Algorithm for Latent Dirichlet Allocation.

Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu

Algorithmica, 2015

Fast and Guaranteed Tensor Decomposition via Sketching.

Yining Wang, Hsiao-Yu Fish Tung, Alexander J. Smola, Anima Anandkumar

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

FEAST at Play: Feature ExtrAction using Score function Tensors.

Majid Janzamin, Hanie Sedghi, U. N. Niranjan, Animashree Anandkumar

Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015

Convolutional Dictionary Learning through Tensor Factorization.

Furong Huang, Animashree Anandkumar

Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015

Are You Going to the Party: Depends, Who Else is Coming?: [Learning Hidden Group Dynamics via Conditional Latent Tree Models].

Forough Arabshahi, Furong Huang, Animashree Anandkumar, Carter T. Butts, Sean M. Fitzhugh

Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

Learning Overcomplete Latent Variable Models through Tensor Methods.

Animashree Anandkumar, Rong Ge, Majid Janzamin

Proceedings of The 28th Conference on Learning Theory, 2015

Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT).

Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky

Proceedings of the Algorithmic Learning Theory - 26th International Conference, 2015

2014

Active Learning of Multiple Source Multiple Destination Topologies.

Pegah Sattari, Maciej Kurant, Animashree Anandkumar, Athina Markopoulou, Michael G. Rabbat

IEEE Trans. Signal Process., 2014

High-dimensional covariance decomposition into sparse Markov and independence models.

Majid Janzamin, Animashree Anandkumar

J. Mach. Learn. Res., 2014

Tensor decompositions for learning latent variable models.

Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky

J. Mach. Learn. Res., 2014

A tensor approach to learning mixed membership community models.

Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade

J. Mach. Learn. Res., 2014

Guarantees for Multi-Step Stochastic ADMM in High Dimensions.

Hanie Sedghi, Anima Anandkumar, Edmond A. Jonckheere

CoRR, 2014

Provable Tensor Methods for Learning Mixtures of Classifiers.

Hanie Sedghi, Anima Anandkumar

CoRR, 2014

Score Function Features for Discriminative Learning: Matrix and Tensor Framework.

Majid Janzamin, Hanie Sedghi, Anima Anandkumar

CoRR, 2014

Integrated Structure and Parameters Learning in Latent Tree Graphical Models.

Furong Huang, U. N. Niranjan, Animashree Anandkumar

CoRR, 2014

Modeling Dynamic Social Interactions via Conditional Latent Tree Models.

Forough Arabshahi, Furong Huang, Animashree Anandkumar, Carter T. Butts

CoRR, 2014

Analyzing Tensor Power Method Dynamics: Applications to Learning Overcomplete Latent Variable Models.

Anima Anandkumar, Rong Ge, Majid Janzamin

CoRR, 2014

Provable Learning of Overcomplete Latent Variable Models: Semi-supervised and Unsupervised Settings.

Animashree Anandkumar, Rong Ge, Majid Janzamin

CoRR, 2014

Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates.

Animashree Anandkumar, Rong Ge, Majid Janzamin

CoRR, 2014

Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition.

Hanie Sedghi, Anima Anandkumar, Edmond A. Jonckheere

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Non-convex Robust PCA.

Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Nonparametric Estimation of Multi-View Latent Variable Models.

Le Song, Animashree Anandkumar, Bo Dai, Bo Xie

Proceedings of the 31th International Conference on Machine Learning, 2014

Learning Sparsely Used Overcomplete Dictionaries.

Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon

Proceedings of The 27th Conference on Learning Theory, 2014

2013

Topology discovery of sparse random graphs with few participants.

Animashree Anandkumar, Avinatan Hassidim, Jonathan A. Kelner

Random Struct. Algorithms, 2013

Seeing through black boxes: Tracking transactions through queues under monitoring resource constraints.

Animashree Anandkumar, Ting He, Chatschik Bisdikian, Dakshi Agrawal

Perform. Evaluation, 2013

Fast Detection of Overlapping Communities via Online Tensor Methods on GPUs.

Furong Huang, U. N. Niranjan, Mohammad Umar Hakeem, Prateek Verma, Animashree Anandkumar

CoRR, 2013

Exact Recovery of Sparsely Used Overcomplete Dictionaries.

Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli

CoRR, 2013

Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization.

Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon

CoRR, 2013

FCD: Fast-concurrent-distributed load balancing under switching costs and imperfect observations.

Furong Huang, Anima Anandkumar

Proceedings of the IEEE INFOCOM 2013, Turin, Italy, April 14-19, 2013, 2013

Learning Linear Bayesian Networks with Latent Variables.

Animashree Anandkumar, Daniel J. Hsu, Adel Javanmard, Sham M. Kakade

Proceedings of the 30th International Conference on Machine Learning, 2013

Robust noncooperative rate-maximization game for MIMO Gaussian interference channels under bounded channel uncertainty.

Amod J. G. Anandkumar, Animashree Anandkumar, Sangarapillai Lambotharan, Jonathon A. Chambers

Proceedings of the IEEE International Conference on Acoustics, 2013

A Tensor Spectral Approach to Learning Mixed Membership Community Models.

Animashree Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade

Proceedings of the COLT 2013, 2013

2012

Feedback Message Passing for Inference in Gaussian Graphical Models.

Ying Liu, Venkat Chandrasekaran, Animashree Anandkumar, Alan S. Willsky

IEEE Trans. Signal Process., 2012

High-dimensional Gaussian graphical model selection: walk summability and local separation criterion.

Animashree Anandkumar, Vincent Y. F. Tan, Furong Huang, Alan S. Willsky

J. Mach. Learn. Res., 2012

A Method of Moments for Mixture Models and Hidden Markov Models.

Animashree Anandkumar, Daniel J. Hsu, Sham M. Kakade

Proceedings of the COLT 2012, 2012

Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation

Animashree Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-Kai Liu

CoRR, 2012

Learning Loopy Graphical Models with Latent Variables: Efficient Methods and Guarantees

Animashree Anandkumar, Ragupathyraj Valluvan

CoRR, 2012

Learning High-Dimensional Mixtures of Graphical Models

Animashree Anandkumar, Daniel J. Hsu, Sham M. Kakade

CoRR, 2012

Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs.

Animashree Anandkumar, Ragupathyraj Valluvan

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Learning Mixtures of Tree Graphical Models.

Animashree Anandkumar, Daniel J. Hsu, Furong Huang, Sham M. Kakade

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

High-Dimensional Covariance Decomposition into Sparse Markov and Independence Domains.

Majid Janzamin, Animashree Anandkumar

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Robust Rate Maximization Game Under Bounded Channel Uncertainty.

Amod J. G. Anandkumar, Animashree Anandkumar, Sangarapillai Lambotharan, Jonathon A. Chambers

IEEE Trans. Veh. Technol., 2011

A Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov Tree Structures.

Vincent Y. F. Tan, Animashree Anandkumar, Lang Tong, Alan S. Willsky

IEEE Trans. Inf. Theory, 2011

Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret.

Animashree Anandkumar, Nithin Michael, Ao Kevin Tang, Ananthram Swami

IEEE J. Sel. Areas Commun., 2011

Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates.

Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky

J. Mach. Learn. Res., 2011

Learning Latent Tree Graphical Models.

Myung Jin Choi, Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky

J. Mach. Learn. Res., 2011

High-Dimensional Structure Estimation in Ising Models: Tractable Graph Families

Animashree Anandkumar, Vincent Y. F. Tan, Alan S. Willsky

CoRR, 2011

High-Dimensional Gaussian Graphical Model Selection: Tractable Graph Families

Animashree Anandkumar, Vincent Y. F. Tan, Alan S. Willsky

CoRR, 2011

High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions.

Animashree Anandkumar, Vincent Y. F. Tan, Alan S. Willsky

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Spectral Methods for Learning Multivariate Latent Tree Structure.

Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Summary based structures with improved sublinear recovery for compressed sensing.

M. Amin Khajehnejad, Juhwan Yoo, Animashree Anandkumar, Babak Hassibi

Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings, 2011

Index-based sampling policies for tracking dynamic networks under sampling constraints.

Ting He, Animashree Anandkumar, Dakshi Agrawal

Proceedings of the INFOCOM 2011. 30th IEEE International Conference on Computer Communications, 2011

Energy-latency tradeoff for in-network function computation in random networks.

Paul Balister, Béla Bollobás, Animashree Anandkumar, Alan S. Willsky

Proceedings of the INFOCOM 2011. 30th IEEE International Conference on Computer Communications, 2011

2010

Learning Gaussian tree models: analysis of error exponents and extremal structures.

Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky

IEEE Trans. Signal Process., 2010

Error exponents for composite hypothesis testing of Markov forest distributions.

Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky

Proceedings of the IEEE International Symposium on Information Theory, 2010

Limit laws for random spatial graphical models.

Animashree Anandkumar, Joseph E. Yukich, Alan S. Willsky

Proceedings of the IEEE International Symposium on Information Theory, 2010

Opportunistic Spectrum Access with Multiple Users: Learning under Competition.

Animashree Anandkumar, Nithin Michael, Ao Tang

Proceedings of the INFOCOM 2010. 29th IEEE International Conference on Computer Communications, 2010

2009

Detection of Gauss-Markov Random Fields With Nearest-Neighbor Dependency.

Animashree Anandkumar, Lang Tong, Ananthram Swami

IEEE Trans. Inf. Theory, 2009

Selectively retrofitting monitoring in distributed systems.

Animashree Anandkumar, Chatschik Bisdikian, Ting He, Dakshi Agrawal

SIGMETRICS Perform. Evaluation Rev., 2009

Energy Scaling Laws for Distributed Inference in Random Fusion Networks.

Animashree Anandkumar, Ananthram Swami, Joseph E. Yukich, Lang Tong

IEEE J. Sel. Areas Commun., 2009

A large-deviation analysis for the maximum likelihood learning of tree structures.

Vincent Y. F. Tan, Animashree Anandkumar, Lang Tong, Alan S. Willsky

Proceedings of the IEEE International Symposium on Information Theory, 2009

Detection error exponent for spatially dependent samples in random networks.

Animashree Anandkumar, Alan S. Willsky, Lang Tong

Proceedings of the IEEE International Symposium on Information Theory, 2009

Prize-Collecting Data Fusion for Cost-Performance Tradeoff in Distributed Inference.

Animashree Anandkumar, Meng Wang, Lang Tong, Ananthram Swami

Proceedings of the INFOCOM 2009. 28th IEEE International Conference on Computer Communications, 2009

2008

Optimal Node Density for Detection in Energy-Constrained Random Networks.

Animashree Anandkumar, Lang Tong, Ananthram Swami

IEEE Trans. Signal Process., 2008

Distributed Estimation Via Random Access.

Animashree Anandkumar, Lang Tong, Ananthram Swami

IEEE Trans. Inf. Theory, 2008

Energy Scaling Laws for Distributed Inference in Random Networks

Animashree Anandkumar, Joseph E. Yukich, Lang Tong, Ananthram Swami

CoRR, 2008

Tracking in a spaghetti bowl: monitoring transactions using footprints.

Animashree Anandkumar, Chatschik Bisdikian, Dakshi Agrawal

Proceedings of the 2008 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 2008

Non-intrusive transaction monitoring using system logs.

Bikram Sengupta, Nilanjan Banerjee, Animashree Anandkumar, Chatschik Bisdikian

Proceedings of the IEEE/IFIP Network Operations and Management Symposium: Pervasive Management for Ubioquitous Networks and Services, 2008

Cost-performance tradeoff in multi-hop aggregation for statistical inference.

Animashree Anandkumar, Lang Tong, Ananthram Swami, Anthony Ephremides

Proceedings of the 2008 IEEE International Symposium on Information Theory, 2008

Minimum Cost Data Aggregation with Localized Processing for Statistical Inference.

Animashree Anandkumar, Lang Tong, Ananthram Swami, Anthony Ephremides

Proceedings of the INFOCOM 2008. 27th IEEE International Conference on Computer Communications, 2008

2007

Type-Based Random Access for Distributed Detection Over Multiaccess Fading Channels.

Animashree Anandkumar, Lang Tong

IEEE Trans. Signal Process., 2007

Detection of Gauss-Markov Random Field on Nearest-Neighbor Graph.

Animashree Anandkumar, Lang Tong, Ananthram Swami

Proceedings of the IEEE International Conference on Acoustics, 2007

Energy Efficient Routing for Statistical Inference of Markov Random Fields.

Animashree Anandkumar, Lang Tong, Ananthram Swami

Proceedings of the 41st Annual Conference on Information Sciences and Systems, 2007

2006

A Large Deviation Analysis of Detection Over Multi-Access Channels with Random Number of Sensors.

Animashree Anandkumar, Lang Tong

Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing, 2006