Anima Anandkumar

According to our database1, Anima Anandkumar authored at least 186 papers between 2006 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of two.

Timeline

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Bibliography

2021
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems.
IEEE Robotics Autom. Lett., 2021

Tensor Dropout for Robust Learning.
IEEE J. Sel. Top. Signal Process., 2021

Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection.
CoRR, 2021

Contrastive Syn-to-Real Generalization.
CoRR, 2021

Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles.
CoRR, 2021

Dynamic Social Media Monitoring for Fast-Evolving Online Discussions.
CoRR, 2021

Disentangling Observed Causal Effects from Latent Confounders using Method of Moments.
CoRR, 2021

Active Learning under Label Shift.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Tensor Regression Networks.
J. Mach. Learn. Res., 2020

Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects.
CoRR, 2020

Stability and Identification of Random Asynchronous Linear Time-Invariant Systems.
CoRR, 2020

Fast Uncertainty Quantification for Deep Object Pose Estimation.
CoRR, 2020

Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces.
CoRR, 2020

Fourier Neural Operator for Parametric Partial Differential Equations.
CoRR, 2020

Distributionally Robust Learning for Unsupervised Domain Adaptation.
CoRR, 2020

Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion.
CoRR, 2020

Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery.
CoRR, 2020

Explore More and Improve Regret in Linear Quadratic Regulators.
CoRR, 2020

Unsupervised Controllable Generation with Self-Training.
CoRR, 2020

OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features.
CoRR, 2020

Deep Bayesian Quadrature Policy Optimization.
CoRR, 2020

Competitive Policy Optimization.
CoRR, 2020

Competitive Mirror Descent.
CoRR, 2020

Regret Bound of Adaptive Control in Linear Quadratic Gaussian (LQG) Systems.
CoRR, 2020

Neural Operator: Graph Kernel Network for Partial Differential Equations.
CoRR, 2020

Convolutional Tensor-Train LSTM for Spatio-temporal Learning.
CoRR, 2020

Regret Minimization in Partially Observable Linear Quadratic Control.
CoRR, 2020

OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

MeshfreeFlowNet: a physics-constrained deep continuous space-time super-resolution framework.
Proceedings of the International Conference for High Performance Computing, 2020

Convolutional Tensor-Train LSTM for Spatio-Temporal Learning.
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.
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.
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.
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.
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.
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.
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.
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Implicit competitive regularization in GANs.
Proceedings of the 37th International Conference on Machine Learning, 2020

Semi-Supervised StyleGAN for Disentanglement Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

Automated Synthetic-to-Real Generalization.
Proceedings of the 37th International Conference on Machine Learning, 2020

Angular Visual Hardness.
Proceedings of the 37th International Conference on Machine Learning, 2020

MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

Higher-Order Count Sketch: Dimensionality Reduction that Retains Efficient Tensor Operations.
Proceedings of the Data Compression Conference, 2020

Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data.
Proceedings of the 2020 American Control Conference, 2020

2019
TensorLy: Tensor Learning in Python.
J. Mach. Learn. Res., 2019

Spectral Learning on Matrices and Tensors.
Found. Trends Mach. Learn., 2019

Learning Pose Estimation for UAV Autonomous Navigation andLanding Using Visual-Inertial Sensor Data.
CoRR, 2019

InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers.
CoRR, 2019

Triply Robust Off-Policy Evaluation.
CoRR, 2019

Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates.
CoRR, 2019

Memory Augmented Recursive Neural Networks.
CoRR, 2019

Out-of-Distribution Detection Using Neural Rendering Generative Models.
CoRR, 2019

Directivity Modes of Earthquake Populations with Unsupervised Learning.
CoRR, 2019

Learning Causal State Representations of Partially Observable Environments.
CoRR, 2019

Stochastically Rank-Regularized Tensor Regression Networks.
CoRR, 2019

Multi-dimensional Tensor Sketch.
CoRR, 2019

Stochastic Linear Bandits with Hidden Low Rank Structure.
CoRR, 2019

Guaranteed Scalable Learning of Latent Tree Models.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Competitive Gradient Descent.
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.
Proceedings of the International Conference on Robotics and Automation, 2019

Multi Sense Embeddings from Topic Models.
Proceedings of the 3rd International Conference on Natural Language and Speech Processing, 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
Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning.
CoRR, 2018

Trust Region Policy Optimization of POMDPs.
CoRR, 2018

signSGD with Majority Vote is Communication Efficient And Byzantine Fault Tolerant.
CoRR, 2018

Sample-Efficient Deep RL with Generative Adversarial Tree Search.
CoRR, 2018

Combining Symbolic and Function Evaluation Expressions In Neural Programs.
CoRR, 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

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. Inf. Theory, 2017

Analyzing Tensor Power Method Dynamics in Overcomplete Regime.
J. Mach. Learn. Res., 2017

Long-term Forecasting using Tensor-Train RNNs.
CoRR, 2017

Compact Tensor Pooling for Visual Question Answering.
CoRR, 2017

Tensor Regression Networks.
CoRR, 2017

Experimental results : Reinforcement Learning of POMDPs using Spectral Methods.
CoRR, 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 J. Optim., 2016

Tensor Computing for Internet of Things (Dagstuhl Perspectives Workshop 16152).
Dagstuhl Reports, 2016

Training Input-Output Recurrent Neural Networks through Spectral Methods.
CoRR, 2016

Unsupervised learning of transcriptional regulatory networks via latent tree graphical models.
CoRR, 2016

Reinforcement Learning of Contextual MDPs using Spectral Methods.
CoRR, 2016

Reinforcement Learning of POMDP's using Spectral Methods.
CoRR, 2016

Beyond LDA: A Unified Framework for Learning Latent Normalized Infinitely Divisible Topic Models through Spectral Methods.
CoRR, 2016

Homotopy Method for Tensor Principal Component Analysis.
CoRR, 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

When are overcomplete topic models identifiable? uniqueness of tensor tucker decompositions with structured sparsity.
J. Mach. Learn. Res., 2015

Provable Methods for Training Neural Networks with Sparse Connectivity.
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.
CoRR, 2015

Generalization Bounds for Neural Networks through Tensor Factorization.
CoRR, 2015

Score Function Features for Discriminative Learning.
Proceedings of the 3rd International Conference on Learning Representations, 2015

Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods.
CoRR, 2015

A Spectral Algorithm for Latent Dirichlet Allocation.
Algorithmica, 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
Active Learning of Multiple Source Multiple Destination Topologies.
IEEE Trans. Signal Process., 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

Guarantees for Multi-Step Stochastic ADMM in High Dimensions.
CoRR, 2014

Provable Tensor Methods for Learning Mixtures of Classifiers.
CoRR, 2014

Score Function Features for Discriminative Learning: Matrix and Tensor Framework.
CoRR, 2014

Integrated Structure and Parameters Learning in Latent Tree Graphical Models.
CoRR, 2014

Modeling Dynamic Social Interactions via Conditional Latent Tree Models.
CoRR, 2014

Analyzing Tensor Power Method Dynamics: Applications to Learning Overcomplete Latent Variable Models.
CoRR, 2014

Provable Learning of Overcomplete Latent Variable Models: Semi-supervised and Unsupervised Settings.
CoRR, 2014

Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates.
CoRR, 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
Topology discovery of sparse random graphs with few participants.
Random Struct. Algorithms, 2013

Seeing through black boxes: Tracking transactions through queues under monitoring resource constraints.
Perform. Evaluation, 2013

Fast Detection of Overlapping Communities via Online Tensor Methods on GPUs.
CoRR, 2013

Exact Recovery of Sparsely Used Overcomplete Dictionaries.
CoRR, 2013

Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization.
CoRR, 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

2012
Feedback Message Passing for Inference in Gaussian Graphical Models.
IEEE Trans. Signal Process., 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

Two SVDs Suffice: Spectral decompositions for probabilistic topic modeling and latent Dirichlet allocation
CoRR, 2012

Learning Loopy Graphical Models with Latent Variables: Efficient Methods and Guarantees
CoRR, 2012

Learning High-Dimensional Mixtures of Graphical Models
CoRR, 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

High-Dimensional Covariance Decomposition into Sparse Markov and Independence Domains.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Robust Rate Maximization Game Under Bounded Channel Uncertainty.
IEEE Trans. Veh. Technol., 2011

A Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov Tree Structures.
IEEE Trans. Inf. Theory, 2011

Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret.
IEEE J. Sel. Areas Commun., 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

High-Dimensional Structure Estimation in Ising Models: Tractable Graph Families
CoRR, 2011

High-Dimensional Gaussian Graphical Model Selection: Tractable Graph Families
CoRR, 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 Process., 2010

Error exponents for composite hypothesis testing of Markov forest distributions.
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

2009
Detection of Gauss-Markov Random Fields With Nearest-Neighbor Dependency.
IEEE Trans. Inf. Theory, 2009

Selectively retrofitting monitoring in distributed systems.
SIGMETRICS Perform. Evaluation Rev., 2009

Energy Scaling Laws for Distributed Inference in Random Fusion Networks.
IEEE J. Sel. Areas Commun., 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 Process., 2008

Distributed Estimation Via Random Access.
IEEE Trans. Inf. Theory, 2008

Energy Scaling Laws for Distributed Inference in Random Networks
CoRR, 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 Process., 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


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