
Stochastically RankRegularized Tensor Regression Networks
Overparametrization of deep neural networks has recently been shown to ...
02/27/2019 ∙ by Arinbjörn Kolbeinsson, et al. ∙ 83 ∙ shareread it

Learning Causal State Representations of Partially Observable Environments
Intelligent agents can cope with sensoryrich environments by learning t...
06/25/2019 ∙ by Amy Zhang, et al. ∙ 68 ∙ shareread it

Implicit competitive regularization in GANs
Generative adversarial networks (GANs) are capable of producing high qua...
10/13/2019 ∙ by Florian Schäfer, et al. ∙ 49 ∙ shareread it

OutofDistribution Detection Using Neural Rendering Generative Models
Outofdistribution (OoD) detection is a natural downstream task for dee...
07/10/2019 ∙ by Yujia Huang, et al. ∙ 35 ∙ shareread it

Stochastic Linear Bandits with Hidden Low Rank Structure
Highdimensional representations often have a lower dimensional underlyi...
01/28/2019 ∙ by Sahin Lale, et al. ∙ 18 ∙ shareread it

Open Vocabulary Learning on Source Code with a GraphStructured Cache
Machine learning models that take computer program source code as input ...
10/18/2018 ∙ by Milan Cvitkovic, et al. ∙ 2 ∙ shareread it

Robust Regression for Safe Exploration in Control
We study the problem of safe learning and exploration in sequential cont...
06/13/2019 ∙ by Anqi Liu, et al. ∙ 1 ∙ shareread it

Compact Tensor Pooling for Visual Question Answering
Performing high level cognitive tasks requires the integration of featur...
06/20/2017 ∙ by Yang Shi, et al. ∙ 0 ∙ shareread it

Homotopy Analysis for Tensor PCA
Developing efficient and guaranteed nonconvex algorithms has been an imp...
10/28/2016 ∙ by Anima Anandkumar, et al. ∙ 0 ∙ shareread it

Training InputOutput Recurrent Neural Networks through Spectral Methods
We consider the problem of training inputoutput recurrent neural networ...
03/03/2016 ∙ by Hanie Sedghi, et al. ∙ 0 ∙ shareread it

Efficient approaches for escaping higher order saddle points in nonconvex optimization
Local search heuristics for nonconvex optimizations are popular in appl...
02/18/2016 ∙ by Anima Anandkumar, et al. ∙ 0 ∙ shareread it

Beating the Perils of NonConvexity: Guaranteed Training of Neural Networks using Tensor Methods
Training neural networks is a challenging nonconvex optimization proble...
06/28/2015 ∙ by Majid Janzamin, et al. ∙ 0 ∙ shareread it

A Scale Mixture Perspective of Multiplicative Noise in Neural Networks
Corrupting the input and hidden layers of deep neural networks (DNNs) wi...
06/10/2015 ∙ by Eric Nalisnick, et al. ∙ 0 ∙ shareread it

Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods
Community detection in graphs has been extensively studied both in theor...
03/16/2015 ∙ by Anima Anandkumar, et al. ∙ 0 ∙ shareread it

Score Function Features for Discriminative Learning
Feature learning forms the cornerstone for tackling challenging learning...
12/19/2014 ∙ by Majid Janzamin, et al. ∙ 0 ∙ shareread it

Provable Tensor Methods for Learning Mixtures of Generalized Linear Models
We consider the problem of learning mixtures of generalized linear model...
12/09/2014 ∙ by Hanie Sedghi, et al. ∙ 0 ∙ shareread it

Score Function Features for Discriminative Learning: Matrix and Tensor Framework
Feature learning forms the cornerstone for tackling challenging learning...
12/09/2014 ∙ by Majid Janzamin, et al. ∙ 0 ∙ shareread it

Provable Methods for Training Neural Networks with Sparse Connectivity
We provide novel guaranteed approaches for training feedforward neural n...
12/08/2014 ∙ by Hanie Sedghi, et al. ∙ 0 ∙ shareread it

Analyzing Tensor Power Method Dynamics in Overcomplete Regime
We present a novel analysis of the dynamics of tensor power iterations i...
11/06/2014 ∙ by Anima Anandkumar, et al. ∙ 0 ∙ shareread it

MultiStep Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Noisy Matrix Decomposition
We propose an efficient ADMM method with guarantees for highdimensional...
02/20/2014 ∙ by Hanie Sedghi, et al. ∙ 0 ∙ shareread it

A Tensor Approach to Learning Mixed Membership Community Models
Community detection is the task of detecting hidden communities from obs...
02/12/2013 ∙ by Anima Anandkumar, et al. ∙ 0 ∙ shareread it

MultiObject Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models
Deep learning has shown stateofart classification performance on datas...
05/02/2015 ∙ by Tejaswi Nimmagadda, et al. ∙ 0 ∙ shareread it

Tensor decompositions for learning latent variable models
This work considers a computationally and statistically efficient parame...
10/29/2012 ∙ by Anima Anandkumar, et al. ∙ 0 ∙ shareread it

StrassenNets: Deep learning with a multiplication budget
A large fraction of the arithmetic operations required to evaluate deep ...
12/11/2017 ∙ by Michael Tschannen, et al. ∙ 0 ∙ shareread it

Learning From Noisy Singlylabeled Data
Supervised learning depends on annotated examples, which are taken to be...
12/13/2017 ∙ by Ashish Khetan, et al. ∙ 0 ∙ shareread it

Active Learning with Partial Feedback
In the largescale multiclass setting, assigning labels often consists o...
02/21/2018 ∙ by Peiyun Hu, et al. ∙ 0 ∙ shareread it

Stochastic Activation Pruning for Robust Adversarial Defense
Neural networks are known to be vulnerable to adversarial examples. Care...
03/05/2018 ∙ by Guneet S. Dhillon, et al. ∙ 0 ∙ shareread it

signSGD: compressed optimisation for nonconvex problems
Training large neural networks requires distributing learning across mul...
02/13/2018 ∙ by Jeremy Bernstein, et al. ∙ 0 ∙ shareread it

Experimental results : Reinforcement Learning of POMDPs using Spectral Methods
We propose a new reinforcement learning algorithm for partially observab...
05/07/2017 ∙ by Kamyar Azizzadenesheli, et al. ∙ 0 ∙ shareread it

Reinforcement Learning in RichObservation MDPs using Spectral Methods
Designing effective explorationexploitation algorithms in Markov decisi...
11/11/2016 ∙ by Kamyar Azizzadenesheli, et al. ∙ 0 ∙ shareread it

Online and DifferentiallyPrivate Tensor Decomposition
In this paper, we resolve many of the key algorithmic questions regardin...
06/20/2016 ∙ by Yining Wang, et al. ∙ 0 ∙ shareread it

Spectral Methods for Correlated Topic Models
In this paper, we propose guaranteed spectral methods for learning a bro...
05/30/2016 ∙ by Forough Arabshahi, et al. ∙ 0 ∙ shareread it

Reinforcement Learning of POMDPs using Spectral Methods
We propose a new reinforcement learning algorithm for partially observab...
02/25/2016 ∙ by Kamyar Azizzadenesheli, et al. ∙ 0 ∙ shareread it

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...
02/04/2016 ∙ by Furong Huang, et al. ∙ 0 ∙ shareread it

Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations
Robust tensor CP decomposition involves decomposing a tensor into low ra...
10/15/2015 ∙ by Prateek Jain, et al. ∙ 0 ∙ shareread it

Fast and Guaranteed Tensor Decomposition via Sketching
Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in sta...
06/14/2015 ∙ by Yining Wang, et al. ∙ 0 ∙ shareread it

Convolutional Dictionary Learning through Tensor Factorization
Tensor methods have emerged as a powerful paradigm for consistent learni...
06/10/2015 ∙ by Furong Huang, et al. ∙ 0 ∙ shareread it

Nonconvex Robust PCA
We propose a new method for robust PCA  the task of recovering a lowr...
10/28/2014 ∙ by Praneeth Netrapalli, et al. ∙ 0 ∙ shareread it

Sample Complexity Analysis for Learning Overcomplete Latent Variable Models through Tensor Methods
We provide guarantees for learning latent variable models emphasizing on...
08/03/2014 ∙ by Rong Ge, et al. ∙ 0 ∙ shareread it

Guaranteed NonOrthogonal Tensor Decomposition via Alternating Rank1 Updates
In this paper, we provide local and global convergence guarantees for re...
02/21/2014 ∙ by Rong Ge, et al. ∙ 0 ∙ shareread it

Nonparametric Estimation of MultiView Latent Variable Models
Spectral methods have greatly advanced the estimation of latent variable...
11/13/2013 ∙ by Le Song, et al. ∙ 0 ∙ shareread it

A Clustering Approach to Learn SparselyUsed Overcomplete Dictionaries
We consider the problem of learning overcomplete dictionaries in the con...
09/08/2013 ∙ by Alekh Agarwal, et al. ∙ 0 ∙ shareread it

Online Tensor Methods for Learning Latent Variable Models
We introduce an online tensor decomposition based approach for two laten...
09/03/2013 ∙ by Furong Huang, et al. ∙ 0 ∙ shareread it

When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
Overcomplete latent representations have been very popular for unsupervi...
08/13/2013 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

HighDimensional Covariance Decomposition into Sparse Markov and Independence Models
Fitting highdimensional data involves a delicate tradeoff between faith...
11/05/2012 ∙ by Majid Janzamin, et al. ∙ 0 ∙ shareread it

Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints
Unsupervised estimation of latent variable models is a fundamental probl...
09/24/2012 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it

HighDimensional Covariance Decomposition into Sparse Markov and Independence Domains
In this paper, we present a novel framework incorporating a combination ...
06/27/2012 ∙ by Majid Janzamin, et al. ∙ 0 ∙ shareread it

A Spectral Algorithm for Latent Dirichlet Allocation
The problem of topic modeling can be seen as a generalization of the clu...
04/30/2012 ∙ by Dean P. Foster, et al. ∙ 0 ∙ shareread it

Learning loopy graphical models with latent variables: Efficient methods and guarantees
The problem of structure estimation in graphical models with latent vari...
03/17/2012 ∙ by Ragupathyraj Valluvan, et al. ∙ 0 ∙ shareread it

A Method of Moments for Mixture Models and Hidden Markov Models
Mixture models are a fundamental tool in applied statistics and machine ...
03/03/2012 ∙ by Daniel Hsu, et al. ∙ 0 ∙ shareread it
Anima Anandkumar
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Bren Professor at Caltech and Principal Scientist at NVIDIA