
On Detection of Faint Edges in Noisy Images
A fundamental question for edge detection in noisy images is how faint c...
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Unsupervised Ensemble Regression
Consider a regression problem where there is no labeled data and the onl...
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Minimaxoptimal semisupervised regression on unknown manifolds
We consider semisupervised regression when the predictor variables are ...
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A Deep Learning Approach to Unsupervised Ensemble Learning
We show how deep learning methods can be applied in the context of crowd...
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Unsupervised Ensemble Learning with Dependent Classifiers
In unsupervised ensemble learning, one obtains predictions from multiple...
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Detecting the large entries of a sparse covariance matrix in subquadratic time
The covariance matrix of a pdimensional random variable is a fundamenta...
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Estimating the Accuracies of Multiple Classifiers Without Labeled Data
In various situations one is given only the predictions of multiple clas...
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On the Optimality of Averaging in Distributed Statistical Learning
A common approach to statistical learning with bigdata is to randomly s...
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Do semidefinite relaxations solve sparse PCA up to the information limit?
Estimating the leading principal components of data, assuming they are s...
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Ranking and combining multiple predictors without labeled data
In a broad range of classification and decision making problems, one is ...
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On learning parametricoutput HMMs
We present a novel approach for learning an HMM whose outputs are distri...
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Active Learning with Distributional Estimates
Active Learning (AL) is increasingly important in a broad range of appli...
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SpectralNet: Spectral Clustering using Deep Neural Networks
Spectral clustering is a leading and popular technique in unsupervised d...
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Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Latent variable models with hidden binary units appear in various applic...
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Semiparametric Classification of Forest Graphical Models
We propose a new semiparametric approach to binary classification that e...
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Tight Recovery Guarantees for Orthogonal Matching Pursuit Under Gaussian Noise
Orthogonal Matching pursuit (OMP) is a popular algorithm to estimate an ...
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