
Finding Social Media Trolls: Dynamic Keyword Selection Methods for RapidlyEvolving Online Debates
Online harassment is a significant social problem. Prevention of online ...
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Stochastically RankRegularized Tensor Regression Networks
Overparametrization of deep neural networks has recently been shown to ...
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Learning Causal State Representations of Partially Observable Environments
Intelligent agents can cope with sensoryrich environments by learning t...
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Angular Visual Hardness
Although convolutional neural networks (CNNs) are inspired by the mechan...
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Implicit competitive regularization in GANs
Generative adversarial networks (GANs) are capable of producing high qua...
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Triply Robust OffPolicy Evaluation
We propose a robust regression approach to offpolicy evaluation (OPE) f...
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OutofDistribution Detection Using Neural Rendering Generative Models
Outofdistribution (OoD) detection is a natural downstream task for dee...
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InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers
Continuous Normalizing Flows (CNFs) have emerged as promising deep gener...
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Stochastic Linear Bandits with Hidden Low Rank Structure
Highdimensional representations often have a lower dimensional underlyi...
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Memory Augmented Recursive Neural Networks
Recursive neural networks have shown an impressive performance for model...
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Regret Minimization in Partially Observable Linear Quadratic Control
We study the problem of regret minimization in partially observable line...
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SampleEfficient Deep RL with Generative Adversarial Tree Search
We propose Generative Adversarial Tree Search (GATS), a sampleefficient...
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Open Vocabulary Learning on Source Code with a GraphStructured Cache
Machine learning models that take computer program source code as input ...
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Robust Regression for Safe Exploration in Control
We study the problem of safe learning and exploration in sequential cont...
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Compact Tensor Pooling for Visual Question Answering
Performing high level cognitive tasks requires the integration of featur...
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Homotopy Analysis for Tensor PCA
Developing efficient and guaranteed nonconvex algorithms has been an imp...
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Training InputOutput Recurrent Neural Networks through Spectral Methods
We consider the problem of training inputoutput recurrent neural networ...
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Efficient approaches for escaping higher order saddle points in nonconvex optimization
Local search heuristics for nonconvex optimizations are popular in appl...
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Beating the Perils of NonConvexity: Guaranteed Training of Neural Networks using Tensor Methods
Training neural networks is a challenging nonconvex optimization proble...
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A Scale Mixture Perspective of Multiplicative Noise in Neural Networks
Corrupting the input and hidden layers of deep neural networks (DNNs) wi...
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Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods
Community detection in graphs has been extensively studied both in theor...
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Score Function Features for Discriminative Learning
Feature learning forms the cornerstone for tackling challenging learning...
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Provable Tensor Methods for Learning Mixtures of Generalized Linear Models
We consider the problem of learning mixtures of generalized linear model...
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Score Function Features for Discriminative Learning: Matrix and Tensor Framework
Feature learning forms the cornerstone for tackling challenging learning...
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Provable Methods for Training Neural Networks with Sparse Connectivity
We provide novel guaranteed approaches for training feedforward neural n...
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Analyzing Tensor Power Method Dynamics in Overcomplete Regime
We present a novel analysis of the dynamics of tensor power iterations i...
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MultiStep Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Noisy Matrix Decomposition
We propose an efficient ADMM method with guarantees for highdimensional...
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A Tensor Approach to Learning Mixed Membership Community Models
Community detection is the task of detecting hidden communities from obs...
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MultiObject Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models
Deep learning has shown stateofart classification performance on datas...
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Tensor decompositions for learning latent variable models
This work considers a computationally and statistically efficient parame...
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StrassenNets: Deep learning with a multiplication budget
A large fraction of the arithmetic operations required to evaluate deep ...
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Learning From Noisy Singlylabeled Data
Supervised learning depends on annotated examples, which are taken to be...
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Active Learning with Partial Feedback
In the largescale multiclass setting, assigning labels often consists o...
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Stochastic Activation Pruning for Robust Adversarial Defense
Neural networks are known to be vulnerable to adversarial examples. Care...
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signSGD: compressed optimisation for nonconvex problems
Training large neural networks requires distributing learning across mul...
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Experimental results : Reinforcement Learning of POMDPs using Spectral Methods
We propose a new reinforcement learning algorithm for partially observab...
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Reinforcement Learning in RichObservation MDPs using Spectral Methods
Designing effective explorationexploitation algorithms in Markov decisi...
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Online and DifferentiallyPrivate Tensor Decomposition
In this paper, we resolve many of the key algorithmic questions regardin...
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Spectral Methods for Correlated Topic Models
In this paper, we propose guaranteed spectral methods for learning a bro...
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Reinforcement Learning of POMDPs using Spectral Methods
We propose a new reinforcement learning algorithm for partially observab...
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Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model
Cataloging the neuronal cell types that comprise circuitry of individual...
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Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations
Robust tensor CP decomposition involves decomposing a tensor into low ra...
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Fast and Guaranteed Tensor Decomposition via Sketching
Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in sta...
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Convolutional Dictionary Learning through Tensor Factorization
Tensor methods have emerged as a powerful paradigm for consistent learni...
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Nonconvex Robust PCA
We propose a new method for robust PCA  the task of recovering a lowr...
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Sample Complexity Analysis for Learning Overcomplete Latent Variable Models through Tensor Methods
We provide guarantees for learning latent variable models emphasizing on...
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Guaranteed NonOrthogonal Tensor Decomposition via Alternating Rank1 Updates
In this paper, we provide local and global convergence guarantees for re...
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Nonparametric Estimation of MultiView Latent Variable Models
Spectral methods have greatly advanced the estimation of latent variable...
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A Clustering Approach to Learn SparselyUsed Overcomplete Dictionaries
We consider the problem of learning overcomplete dictionaries in the con...
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Online Tensor Methods for Learning Latent Variable Models
We introduce an online tensor decomposition based approach for two laten...
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Anima Anandkumar
verfied profile
Bren Professor at Caltech and Principal Scientist at NVIDIA