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04/27/2023
A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion
In 1-bit matrix completion, the aim is to estimate an underlying low-ran...
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01/29/2023
Imbalanced Mixed Linear Regression
We consider the problem of mixed linear regression (MLR), where each obs...
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01/09/2023
Distributed Sparse Linear Regression under Communication Constraints
In multiple domains, statistical tasks are performed in distributed sett...
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09/15/2022
Distributed Sparse Linear Regression with Sublinear Communication
We study the problem of high-dimensional sparse linear regression in a d...
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06/21/2022
Tyler's and Maronna's M-estimators: Non-Asymptotic Concentration Results
Tyler's and Maronna's M-estimators, as well as their regularized variant...
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01/31/2022
Inductive Matrix Completion: No Bad Local Minima and a Fast Algorithm
The inductive matrix completion (IMC) problem is to recover a low rank m...
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06/24/2021
GNMR: A provable one-line algorithm for low rank matrix recovery
Low rank matrix recovery problems, including matrix completion and matri...
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02/26/2021
Spectral Top-Down Recovery of Latent Tree Models
Modeling the distribution of high dimensional data by a latent tree grap...
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02/05/2021
Sparse Normal Means Estimation with Sublinear Communication
We consider the problem of sparse normal means estimation in a distribut...
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01/03/2021
Improved Convergence Guarantees for Learning Gaussian Mixture Models by EM and Gradient EM
We consider the problem of estimating the parameters a Gaussian Mixture ...
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07/20/2020
"Self-Wiener" Filtering: Non-Iterative Data-Driven Robust Deconvolution of Deterministic Signals
We consider the fundamental problem of robust deconvolution, and particu...
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05/18/2020
The Trimmed Lasso: Sparse Recovery Guarantees and Practical Optimization by the Generalized Soft-Min Penalty
We present a new approach to solve the sparse approximation or best subs...
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02/28/2020
Spectral neighbor joining for reconstruction of latent tree models
A key assumption in multiple scientific applications is that the distrib...
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05/29/2019
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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06/06/2018
Semiparametric Classification of Forest Graphical Models
We propose a new semiparametric approach to binary classification that e...
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02/27/2018
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Latent variable models with hidden binary units appear in various applic...
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01/04/2018
SpectralNet: Spectral Clustering using Deep Neural Networks
Spectral clustering is a leading and popular technique in unsupervised d...
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06/22/2017
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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03/08/2017
Unsupervised Ensemble Regression
Consider a regression problem where there is no labeled data and the onl...
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11/07/2016
Minimax-optimal semi-supervised regression on unknown manifolds
We consider semi-supervised regression when the predictor variables are ...
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02/06/2016
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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10/20/2015
Unsupervised Ensemble Learning with Dependent Classifiers
In unsupervised ensemble learning, one obtains predictions from multiple...
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05/12/2015
Detecting the large entries of a sparse covariance matrix in sub-quadratic time
The covariance matrix of a p-dimensional random variable is a fundamenta...
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07/29/2014
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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07/10/2014
On the Optimality of Averaging in Distributed Statistical Learning
A common approach to statistical learning with big-data is to randomly s...
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06/16/2013
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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03/13/2013
Ranking and combining multiple predictors without labeled data
In a broad range of classification and decision making problems, one is ...
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02/25/2013
On learning parametric-output HMMs
We present a novel approach for learning an HMM whose outputs are distri...
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10/16/2012
