b'Boaz Nadler'

research

∙ 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...

0 Xiaoqian Liu, et al. ∙

research

∙ 01/29/2023

Imbalanced Mixed Linear Regression

We consider the problem of mixed linear regression (MLR), where each obs...

0 Pini Zilber, et al. ∙

research

∙ 01/09/2023

Distributed Sparse Linear Regression under Communication Constraints

In multiple domains, statistical tasks are performed in distributed sett...

0 Rodney Fonseca, et al. ∙

research

∙ 09/15/2022

Distributed Sparse Linear Regression with Sublinear Communication

We study the problem of high-dimensional sparse linear regression in a d...

0 Chen Amiraz, et al. ∙

research

∙ 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...

0 Elad Romanov, et al. ∙

research

∙ 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...

0 Pini Zilber, et al. ∙

research

∙ 06/24/2021

GNMR: A provable one-line algorithm for low rank matrix recovery

Low rank matrix recovery problems, including matrix completion and matri...

0 Pini Zilber, et al. ∙

research

∙ 02/26/2021

Spectral Top-Down Recovery of Latent Tree Models

Modeling the distribution of high dimensional data by a latent tree grap...

0 Yariv Aizenbud, et al. ∙

research

∙ 02/05/2021

Sparse Normal Means Estimation with Sublinear Communication

We consider the problem of sparse normal means estimation in a distribut...

0 Chen Amiraz, et al. ∙

research

∙ 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 ...

0 Nimrod Segol, et al. ∙

research

∙ 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...

0 Amir Weiss, et al. ∙

research

∙ 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...

0 Tal Amir, et al. ∙

research

∙ 02/28/2020

Spectral neighbor joining for reconstruction of latent tree models

A key assumption in multiple scientific applications is that the distrib...

0 Ariel Jaffe, et al. ∙

research

∙ 05/29/2019

Tight Recovery Guarantees for Orthogonal Matching Pursuit Under Gaussian Noise

Orthogonal Matching pursuit (OMP) is a popular algorithm to estimate an ...

0 Chen Amiraz, et al. ∙

research

∙ 06/06/2018

Semiparametric Classification of Forest Graphical Models

We propose a new semiparametric approach to binary classification that e...

0 Mary Frances Dorn, et al. ∙

research

∙ 02/27/2018

Learning Binary Latent Variable Models: A Tensor Eigenpair Approach

Latent variable models with hidden binary units appear in various applic...

0 Ariel Jaffe, et al. ∙

research

∙ 01/04/2018

SpectralNet: Spectral Clustering using Deep Neural Networks

Spectral clustering is a leading and popular technique in unsupervised d...

0 Uri Shaham, et al. ∙

research

∙ 06/22/2017

On Detection of Faint Edges in Noisy Images

A fundamental question for edge detection in noisy images is how faint c...

0 Nati Ofir, et al. ∙

research

∙ 03/08/2017

Unsupervised Ensemble Regression

Consider a regression problem where there is no labeled data and the onl...

0 Omer Dror, et al. ∙

research

∙ 11/07/2016

Minimax-optimal semi-supervised regression on unknown manifolds

We consider semi-supervised regression when the predictor variables are ...

0 Amit Moscovich, et al. ∙

research

∙ 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...

0 Uri Shaham, et al. ∙

research

∙ 10/20/2015

Unsupervised Ensemble Learning with Dependent Classifiers

In unsupervised ensemble learning, one obtains predictions from multiple...

0 Ariel Jaffe, et al. ∙

research

∙ 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...

0 Ofer Shwartz, et al. ∙

research

∙ 07/29/2014

Estimating the Accuracies of Multiple Classifiers Without Labeled Data

In various situations one is given only the predictions of multiple clas...

0 Ariel Jaffe, et al. ∙

research

∙ 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...

0 Jonathan Rosenblatt, et al. ∙

research

∙ 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...

0 Robert Krauthgamer, et al. ∙

research

∙ 03/13/2013

Ranking and combining multiple predictors without labeled data

In a broad range of classification and decision making problems, one is ...

0 Fabio Parisi, et al. ∙

research

∙ 02/25/2013

On learning parametric-output HMMs

We present a novel approach for learning an HMM whose outputs are distri...

0 Aryeh Kontorovich, et al. ∙

research

∙ 10/16/2012

Active Learning with Distributional Estimates

Active Learning (AL) is increasingly important in a broad range of appli...

0 Jens Roeder, et al. ∙

Boaz Nadler

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