Anima Anandkumar
According to our database1,
Anima Anandkumar
authored at least 95 papers
between 2006 and 2019.
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Bibliography
2019
TensorLy: Tensor Learning in Python.
J. Mach. Learn. Res., 2019
Guaranteed Scalable Learning of Latent Tree Models.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019
Neural Lander: Stable Drone Landing Control Using Learned Dynamics.
Proceedings of the International Conference on Robotics and Automation, 2019
Open Vocabulary Learning on Source Code with a Graph-Structured Cache.
Proceedings of the 36th International Conference on Machine Learning, 2019
Active Learning with Partial Feedback.
Proceedings of the 7th International Conference on Learning Representations, 2019
signSGD with Majority Vote is Communication Efficient and Fault Tolerant.
Proceedings of the 7th International Conference on Learning Representations, 2019
Regularized Learning for Domain Adaptation under Label Shifts.
Proceedings of the 7th International Conference on Learning Representations, 2019
2018
Tensor Computing for Internet of Things (Dagstuhl Perspectives Workshop 16152).
Dagstuhl Manifestos, 2018
Efficient Exploration Through Bayesian Deep Q-Networks.
Proceedings of the 2018 Information Theory and Applications Workshop, 2018
StrassenNets: Deep Learning with a Multiplication Budget.
Proceedings of the 35th International Conference on Machine Learning, 2018
Born-Again Neural Networks.
Proceedings of the 35th International Conference on Machine Learning, 2018
SIGNSGD: Compressed Optimisation for Non-Convex Problems.
Proceedings of the 35th International Conference on Machine Learning, 2018
Deep Active Learning for Named Entity Recognition.
Proceedings of the 6th International Conference on Learning Representations, 2018
Learning From Noisy Singly-labeled Data.
Proceedings of the 6th International Conference on Learning Representations, 2018
Stochastic Activation Pruning for Robust Adversarial Defense.
Proceedings of the 6th International Conference on Learning Representations, 2018
Compression by the signs: distributed learning is a two-way street.
Proceedings of the 6th International Conference on Learning Representations, 2018
Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs.
Proceedings of the 6th International Conference on Learning Representations, 2018
Question Type Guided Attention in Visual Question Answering.
Proceedings of the Computer Vision - ECCV 2018, 2018
Probabilistic FastText for Multi-Sense Word Embeddings.
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018
2017
A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries.
IEEE Trans. Information Theory, 2017
Analyzing Tensor Power Method Dynamics in Overcomplete Regime.
J. Mach. Learn. Res., 2017
Deep Active Learning for Named Entity Recognition.
Proceedings of the 2nd Workshop on Representation Learning for NLP, 2017
Tensor Contraction Layers for Parsimonious Deep Nets.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017
Homotopy Analysis for Tensor PCA.
Proceedings of the 30th Conference on Learning Theory, 2017
Spectral Methods for Correlated Topic Models.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017
2016
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization.
SIAM Journal on Optimization, 2016
Tensor Computing for Internet of Things (Dagstuhl Perspectives Workshop 16152).
Dagstuhl Reports, 2016
Online and Differentially-Private Tensor Decomposition.
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.
Proceedings of the 23rd IEEE International Conference on High Performance Computing, 2016
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies.
Proceedings of the 29th Conference on Learning Theory, 2016
Reinforcement Learning of POMDPs using Spectral Methods.
Proceedings of the 29th Conference on Learning Theory, 2016
Efficient approaches for escaping higher order saddle points in non-convex optimization.
Proceedings of the 29th Conference on Learning Theory, 2016
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016
Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016
2015
Online tensor methods for learning latent variable models.
J. Mach. Learn. Res., 2015
Provable Methods for Training Neural Networks with Sparse Connectivity.
Proceedings of the 3rd International Conference on Learning Representations, 2015
Score Function Features for Discriminative Learning.
Proceedings of the 3rd International Conference on Learning Representations, 2015
Fast and Guaranteed Tensor Decomposition via Sketching.
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.
Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015
Convolutional Dictionary Learning through Tensor Factorization.
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].
Proceedings of the 2015 IEEE International Conference on Data Mining, 2015
Learning Overcomplete Latent Variable Models through Tensor Methods.
Proceedings of The 28th Conference on Learning Theory, 2015
Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT).
Proceedings of the Algorithmic Learning Theory - 26th International Conference, 2015
2014
High-dimensional covariance decomposition into sparse Markov and independence models.
J. Mach. Learn. Res., 2014
Tensor decompositions for learning latent variable models.
J. Mach. Learn. Res., 2014
A tensor approach to learning mixed membership community models.
J. Mach. Learn. Res., 2014
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014
Non-convex Robust PCA.
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.
Proceedings of the 31th International Conference on Machine Learning, 2014
Learning Sparsely Used Overcomplete Dictionaries.
Proceedings of The 27th Conference on Learning Theory, 2014
2013
Seeing through black boxes: Tracking transactions through queues under monitoring resource constraints.
Perform. Eval., 2013
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013
FCD: Fast-concurrent-distributed load balancing under switching costs and imperfect observations.
Proceedings of the IEEE INFOCOM 2013, Turin, Italy, April 14-19, 2013, 2013
Learning Linear Bayesian Networks with Latent Variables.
Proceedings of the 30th International Conference on Machine Learning, 2013
Robust noncooperative rate-maximization game for MIMO Gaussian interference channels under bounded channel uncertainty.
Proceedings of the IEEE International Conference on Acoustics, 2013
A Tensor Spectral Approach to Learning Mixed Membership Community Models.
Proceedings of the COLT 2013, 2013
Active learning of multiple source multiple destination topologies.
Proceedings of the 47th Annual Conference on Information Sciences and Systems, 2013
2012
High-dimensional Gaussian graphical model selection: walk summability and local separation criterion.
J. Mach. Learn. Res., 2012
A Method of Moments for Mixture Models and Hidden Markov Models.
Proceedings of the COLT 2012, 2012
Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs.
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.
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
A Spectral Algorithm for Latent Dirichlet Allocation.
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.
Proceedings of the 29th International Conference on Machine Learning, 2012
2011
A Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov Tree Structures.
IEEE Trans. Information Theory, 2011
Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret.
IEEE Journal on Selected Areas in Communications, 2011
Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates.
J. Mach. Learn. Res., 2011
Learning Latent Tree Graphical Models.
J. Mach. Learn. Res., 2011
Topology discovery of sparse random graphs with few participants.
Proceedings of the SIGMETRICS 2011, 2011
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions.
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.
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.
Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings, 2011
Index-based sampling policies for tracking dynamic networks under sampling constraints.
Proceedings of the INFOCOM 2011. 30th IEEE International Conference on Computer Communications, 2011
Energy-latency tradeoff for in-network function computation in random networks.
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.
IEEE Trans. Signal Processing, 2010
Error exponents for composite hypothesis testing of Markov forest distributions.
Proceedings of the IEEE International Symposium on Information Theory, 2010
Feedback message passing for inference in gaussian graphical models.
Proceedings of the IEEE International Symposium on Information Theory, 2010
Limit laws for random spatial graphical models.
Proceedings of the IEEE International Symposium on Information Theory, 2010
Opportunistic Spectrum Access with Multiple Users: Learning under Competition.
Proceedings of the INFOCOM 2010. 29th IEEE International Conference on Computer Communications, 2010
Robust rate-maximization game under bounded channel uncertainty.
Proceedings of the IEEE International Conference on Acoustics, 2010
2009
Detection of Gauss-Markov Random Fields With Nearest-Neighbor Dependency.
IEEE Trans. Information Theory, 2009
Selectively retrofitting monitoring in distributed systems.
SIGMETRICS Performance Evaluation Review, 2009
Energy Scaling Laws for Distributed Inference in Random Fusion Networks.
IEEE Journal on Selected Areas in Communications, 2009
A large-deviation analysis for the maximum likelihood learning of tree structures.
Proceedings of the IEEE International Symposium on Information Theory, 2009
Detection error exponent for spatially dependent samples in random networks.
Proceedings of the IEEE International Symposium on Information Theory, 2009
Prize-Collecting Data Fusion for Cost-Performance Tradeoff in Distributed Inference.
Proceedings of the INFOCOM 2009. 28th IEEE International Conference on Computer Communications, 2009
2008
Optimal Node Density for Detection in Energy-Constrained Random Networks.
IEEE Trans. Signal Processing, 2008
Distributed Estimation Via Random Access.
IEEE Trans. Information Theory, 2008
Tracking in a spaghetti bowl: monitoring transactions using footprints.
Proceedings of the 2008 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 2008
Non-intrusive transaction monitoring using system logs.
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.
Proceedings of the 2008 IEEE International Symposium on Information Theory, 2008
Minimum Cost Data Aggregation with Localized Processing for Statistical Inference.
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.
IEEE Trans. Signal Processing, 2007
Detection of Gauss-Markov Random Field on Nearest-Neighbor Graph.
Proceedings of the IEEE International Conference on Acoustics, 2007
Energy Efficient Routing for Statistical Inference of Markov Random Fields.
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.
Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing, 2006