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04/16/2020
Spectral Learning on Matrices and Tensors
Spectral methods have been the mainstay in several domains such as machi...
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06/28/2015
Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods
Training neural networks is a challenging non-convex optimization proble...
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12/19/2014
Score Function Features for Discriminative Learning
Feature learning forms the cornerstone for tackling challenging learning...
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12/09/2014
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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12/09/2014
Score Function Features for Discriminative Learning: Matrix and Tensor Framework
Feature learning forms the cornerstone for tackling challenging learning...
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11/06/2014
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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08/03/2014
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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02/21/2014
Guaranteed Non-Orthogonal Tensor Decomposition via Alternating Rank-1 Updates
In this paper, we provide local and global convergence guarantees for re...
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08/13/2013
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
Overcomplete latent representations have been very popular for unsupervi...
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11/05/2012
High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models
Fitting high-dimensional data involves a delicate tradeoff between faith...
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06/27/2012