2023
2022
2021
2020
2019
2018
2017
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"Efficient Exploration through Bayesian Deep Q-Networks"
Kamyar Azizzadenesheli, Animashree Anandkumar
Appeared at NIPS 2017, Deep RL Workshop, Long Beach, California
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"Long-term forecasting using tensor-train RNNs"
Rose Yu, Stephan Zheng, Anima Anandkumar, Yisong Yue
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"Tensor Regression Networks"
Jean Kossaifi, Zack Chase Lipton, Aran Khanna, Tommaso Furlanello, Anima Anandkumar
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"Compact Tensor Pooling for Visual Question Answering"
Yang Shi, Tommaso Furlanello, Anima Anandkumar
Appeared at CVPR 2017 VQA Workshop, Honolulu, Hawaii
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"Analyzing tensor power method dynamics in overcomplete regime"
Anima Anandkumar, Rong Ge, Majid Janzamin
Appeared at JMLR
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"A clustering approach to learning sparsely used overcomplete dictionaries"
Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli
Appeared at IEEE Transactions on Information Theory 2017
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"Tensor Contraction Layers for Parsimonious Deep Nets"
Jean Kossaifi, Aran Khanna, Zachary C. Lipton, Tommaso Furlanello, Anima Anandkumar
Appeared at CVPR 2017, Workshop, Honolulu, Hawaii
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"Spectral Latent Dirichlet Allocation model on Spark"
Furong Huang, Jencir Lee, and Anima Anandkumar
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"Reinforcement Learning in Rich-Observation MDPs using Spectral Methods"
Kamyar Azizzadenesheli, Alessandro Lazaric, Anima Anandkumar
Appeared at RLDM 2017, Ann Arbor, Michigan, USA
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"Homotopy Analysis for Tensor PCA"
Anima Anandkumar, Yuan Deng, Rong Ge, Hossein Mobahi
Appeared at COLT 2017, Amsterdam, Netherlands
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"Spectral Methods for Correlated Topic Models"
Forough Arabshahi, Animashree Anandkumar
Appeared at the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 54:1439-1447, 2017.
2016
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"Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization"
Alekh Agarwal, Animashree Anandkumar, Prateek Jain, and Praneeth Netrapalli
Appeared at SIAM J. Optim., 26(4), 2016.
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"Experimental results: Reinforcement Learning of POMDPs using Spectral Methods"
Kamyar Azizzadenesheli, Alessandro Lazaric, Anima Anandkumar
Appeared at NIPS 2016, Barcelona, Spain, Workshop on Deep RL
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"Online and Differentially-Private Tensor Decomposition"
Yining Wang, Anima Anandkumar
Appeared at NIPS 2016, Barcelona, Spain
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"Tensor Contractions with Extended BLAS Kernels on CPU and GPU"
Yang Shi, U.N. Niranjan, Anima Anandkumar, Cris Cecka
Appeared at HiPC2016, Hyderabad, India, December 2016
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"PhD Thesis: Stochastic Optimization in High Dimension"
Hanie Sedghi, 2016
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"PhD Thesis: Discovery of Latent Factors in High-dimensional Data Using Tensor Methods"
Furong Huang, 2016
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"PhD Thesis: Non-convex Optimization in Machine Learning: Provable Guarantees Using Tensor Methods"
Majid Janzamin, 2016
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"Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies"
Kamyar Azizzadenesheli, Alessandro Lazaric, Anima Anandkumar
Appeared at COLT 2016, New York, USA
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"Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition"
Furong Huang, A. Anandkumar
Appeared at ACL 2016
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"Training Input-Output Recurrent Neural Networks through Spectral Methods"
H. Sedghi, A. Anandkumar
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"Reinforcement Learning of POMDPs using Spectral Methods"
Kamyar Azizzadenesheli, Alessandro Lazaric, Anima Anandkumar
Appeared at COLT 2016, New York, USA
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"Efficient approaches for escaping higher order saddle points in non-convex optimization"
Anima Anandkumar, Rong Ge
Appeared at COLT 2016, New York, USA
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"Provable Tensor Methods for Learning Mixtures of Generalized Linear Models"
H. Sedghi, M. Janzamin, A. Anandkumar
Appeared at AISTATS 2016
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"Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations"
Anima Anandkumar, Prateek Jain, Yang Shi, U.N. Niranjan
Appeared at AISTATS 2016
2015
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"Feast at Play: Feature ExtrAction using Score function Tensors"
Majid Janzamin, Hanie Sedghi, U.N. Niranjan, Anima Anandkumar
Appeared at NIPS 2015
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"Convolutional Dictionary Learning through Tensor Factorization"
Furong Huang, Anima Anandkumar
Appeared at NIPS 2015
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"Scalable Latent Tree Model and its Application to Health Analytics"
F. Huang, U. N. Niranjan, J. Perros, R. Chen, J. Sun, A. Anandkumar
Appeared at NIPS 2015
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"Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model"
F. Huang, A. Anandkumar, C. Borgs, J. Chayes, E. Fraenkel, M. Hawrylycz, E. Lein, A. Ingrosso, S. Turaga
Appeared at NIPS 2015
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"Are You Going to the Party: Depends, Who Else is Coming?: [Learning Hidden Group Dynamics via Conditional Latent Tree Models]"
Forough Arabshahi, Furong Huang, Anima Anandkumar, Carter T. Butts and Sean M. Fitzhugh
Appeared at International Conference on Data Mining (ICDM), IEEE 2015
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"Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models"
Tejaswi Nimmagadda, Anima Anandkumar
Appeared at SUNw 2015
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"Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Method"
Majid Janzamin, Hanie Sedghi, Anima Anandkumar
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"Fast and Guaranteed Tensor Decomposition via Sketching"
Yining Wang, Hsiao-Yu Tung, Alex Smola, Anima Anandkuma
Appeared at NIPS 2015
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"A Scale Mixture Perspective of Multiplicative Noise in Neural Networks"
Eric Nalisnick, Anima Anandkumar, Padhraic Smyth
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"Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods"
A. Anandkumar, H. Sedghi
2014
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"Online Tensor Methods for Learning Latent Variable Models"
F. Huang, U. N. Niranjan, M. U. Hakeem, A. Anandkumar
Appeared at JMLR 2014
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"Score Function Features for Discriminative Learning: Matrix and Tensor Framework"
M. Janzamin, H. Sedghi, A. Anandkumar
Appeared at JMLR 2014
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"Provable Methods for Training Neural Networks with Sparse Connectivity"
H. Sedghi, A. Anandkumar
Appeared at NIPS Deep Learning Workshop. Dec. 2014
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"PAnalyzing Tensor Power Method Dynamics in Overcomplete Regime"
A. Anandkumar, R. Ge, M. Janzamin
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"Non-convex Robust PCA"
P. Netrapalli, U. N. Niranjan, S. Sanghavi, A. Anandkumar, P. Jain.Â
An abridged version appears in NIPS 2014
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"Sample Complexity Analysis for Learning Overcomplete Latent Variable Models through Tensor Methods"
A. Anandkumar, R. Ge, M. Janzamin
An abridge version appears in COLT 2015
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"Multi-Step Stochastic ADMM in High Dimensions: Applications in Sparse Optimization and Noisy Matrix Decomposition"
H. Sedghi, A. Anandkumar, E. Jonckheere
Appeared at NIPS 2014
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"Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates"
A. Anandkumar, R. Ge, M. Janzamin
Appeared at NIPS 2014
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"Nonparametric Estimation of Multi-View Latent Variable Models"
L. Song, A. Anandkumar, B. Dai, B. Xie
Appeared at International Conference on Machine Learning (ICML) 2014
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"Tensor Decompositions for Learning Latent Variable Models"
A. Anandkumar, R. Ge, D. Hsu, S.M. Kakade and M. Telgarsky.Â
Appeared at Journal of Machine Learning Research 2014
2013
- Learning Loopy Graphical Models with Latent Variables: Efficient Methods and Guarantees, Ann. Statist., 41(2), 2013. [PDF] supplement slides code
- Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization, 2013. [PDF] slides
- Exact Recovery of Sparsely Used Overcomplete Dictionaries, 2013. [PDF]
- When Are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity, 2013. [PDF]
- A Tensor Spectral Approach to Learning Mixed Membership Community Models, COLT 2013. [PDF] slides
- Learning Latent Bayesian Networks and Topic Models Under Expansion Constraints, International Conference on Machine Learning (ICML) 2013. [PDF]
- Fast, Concurrent and Distributed Load Balancing under Switching Costs and Imperfect Observations, IEEE INFOCOM 2013. [PDF]
2012
- High-Dimensional Structure Learning of Ising Models: Local Separation Criterion, Ann. Statist. 40(3), 2012. [PDF] supplement code slides
- Learning Linear Bayesian Networks with Latent Variables, 2012. [PDF]
- Feedback Message Passing for Inference in Gaussian Graphical Models, IEEE Trans. on Signal Processing, 60(8), 2012. [PDF]
- High-Dimensional Covariance Decomposition into Sparse Markov and Independence Domains, International Conference on Machine Learning (ICML) 2012. [PDF] [PDF] slides
- A Method of Moments for Mixture Models and Hidden Markov Models, COLT 2012. [PDF] [PDF]
- Learning High-Dimensional Mixtures of Graphical Models, NIPS 2012. [PDF] [PDF]
- Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation, NIPS 2012. [PDF] [PDF]
2011
- High-Dimensional Gaussian Graphical Model Selection: Walk-Summability and Local Separation Criterion, JMLR 13:229-337, 2012. NIPS 2011. [PDF] [PDF] talk slides
- High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions, NIPS 2011. [PDF]
- Spectral Methods for Learning Multivariate Latent Tree Structure, NIPS 2011. [PDF]
- Robust Rate Maximization Game Under Bounded Channel Uncertainty, IEEE Trans. Vehicular Technology, 60:9, 2011. [PDF]
- Summary Based Structures with Improved Sublinear Recovery for Compressed Sensing, IEEE ISIT 2011. [PDF]
- Topology Discovery of Sparse Random Graphs With Few Participants, ACM SIGMETRICS 2011. [PDF] [PDF] slides
- Learning Latent Tree Graphical Models, JMLR 2011. [PDF] project homepage Allerton version Allerton slides seminar slides
- Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates, JMLR 2011. [PDF] Allerton version Allerton slides
- Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret, IEEE JSAC 2011. [PDF]
- Energy-Latency Tradeoff for In-Network Function Computation in Random Networks, IEEE INFOCOM 2011. [PDF] slides
- Index-Based Sampling Policies for Tracking Dynamic Networks under Sampling Constraints, IEEE INFOCOM 2011. [PDF] supplemental material
- A Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov Tree Structures, IEEE Trans. Information Theory,57:3, 2011. [PDF]
2010
- Scaling Laws for Random Spatial Graphical Models, IEEE ISIT 2010. [PDF] slides
- Error Exponents for Composite Hypothesis Testing of Markov Forest Distributions, IEEE ISIT 2010. [PDF] slides proofs
- Learning Gaussian Tree Models: Analysis of Error Exponents and Extremal Structures, IEEE Trans. Signal Processing, 58:5, 2010. [PDF] slides
- Robust Rate Maximization Game Under Bounded Channel Uncertainty, IEEE ICASSP 2010. [PDF]
- Opportunistic Spectrum Access with Multiple Users: Learning under Competition, IEEE INFOCOM 2010. [PDF] slides
- Seeing Through Black Boxes : Tracking Transactions through Queues under Monitoring Resource Constraints, Elsevier Performance Evaluation 2010. [PDF]
2009
- Energy Scaling Laws for Distributed Inference in Random Fusion Networks, IEEE JSAC, 27:7, 2009. [PDF]
- Selectively Retrofitting Monitoring in Distributed Systems, Workshop on MAMA 2009. [PDF]
- Detection Error Exponent for Spatially Dependent Samples in Random Networks, IEEE ISIT 2009. [PDF]
- Prize-Collecting Data Fusion for Cost-Performance Tradeoff in Distributed Inference, IEEE INFOCOM 2009. [PDF] tech report
- Detection of Gauss-Markov random fields with nearest-neighbor dependency, IEEE Trans. Information Theory, 55:2, 2009. [PDF]
2008 and Earlier
- Optimal Node Density for Detection in Energy Constrained Random Networks, IEEE Trans. Signal Processing, 56:10, 2008. [PDF]
- Distributed Estimation Via Random Access, IEEE Trans. Information Theory, 54:7, 2008. [PDF]
- Tracking in a Spaghetti Bowl: Monitoring Transactions Using Footprints, ACM SIGMETRICS 2008. [PDF]
- Minimum Cost Data Aggregation with Localized Processing for Statistical Inference, IEEE INFOCOM 2008. [PDF]
- A Large Deviation Analysis of Detection over Multi-Access Channels with Random Number of Sensors, ICASSP 2006 (Best Paper Award). [PDF]