Ofir Lindenbaum

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Yale University

Refined Least Squares for Support Recovery

We study the problem of exact support recovery based on noisy observatio...
03/19/2021 ∙ by Ofir Lindenbaum, et al. ∙ 0 ∙ share

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Deep Gated Canonical Correlation Analysis

Canonical Correlation Analysis (CCA) models can extract informative corr...
10/12/2020 ∙ by Ofir Lindenbaum, et al. ∙ 5 ∙ share

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Kernel-based parameter estimation of dynamical systems with unknown observation functions

A low-dimensional dynamical system is observed in an experiment as a hig...
09/09/2020 ∙ by Ofir Lindenbaum, et al. ∙ 7 ∙ share

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Let the Data Choose its Features: Differentiable Unsupervised Feature Selection

Scientific observations often consist of a large number of variables (fe...
07/09/2020 ∙ by Ofir Lindenbaum, et al. ∙ 7 ∙ share

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LOCA: LOcal Conformal Autoencoder for standardized data coordinates

We propose a deep-learning based method for obtaining standardized data ...
04/15/2020 ∙ by Erez Peterfreund, et al. ∙ 10 ∙ share

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Randomly Aggregated Least Squares for Support Recovery

We study the problem of exact support recovery: given an (unknown) vecto...
03/16/2020 ∙ by Ofir Lindenbaum, et al. ∙ 0 ∙ share

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The Spectral Underpinning of word2vec

word2vec due to Mikolov et al. (2013) is a word embedding method that is...
02/27/2020 ∙ by Ariel Jaffe, et al. ∙ 0 ∙ share

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Deep supervised feature selection using Stochastic Gates

In this study, we propose a novel non-parametric embedded feature select...
10/09/2018 ∙ by Yutaro Yamada, et al. ∙ 0 ∙ share

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Geometry-Based Data Generation

Many generative models attempt to replicate the density of their input d...
02/14/2018 ∙ by Ofir Lindenbaum, et al. ∙ 0 ∙ share

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Multi-View Kernel Consensus For Data Analysis and Signal Processing

The input data features set for many data driven tasks is high-dimension...
06/28/2016 ∙ by Moshe Salhov, et al. ∙ 0 ∙ share

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MultiView Diffusion Maps

In this study we consider learning a reduced dimensionality representati...
08/23/2015 ∙ by Ofir Lindenbaum, et al. ∙ 0 ∙ share

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Ofir Lindenbaum

Featured Co-authors

Stefan Steinerberger

Guy Wolf

Yuval Kluger

Smita Krishnaswamy

Ronen Talmon

Uri Shaham

Ioannis G. Kevrekidis

Amir Averbuch

Gal Mishne

Yoel Shkolnisky

Sahand Negahban

Refined Least Squares for Support Recovery

Deep Gated Canonical Correlation Analysis

Kernel-based parameter estimation of dynamical systems with unknown observation functions

Let the Data Choose its Features: Differentiable Unsupervised Feature Selection

LOCA: LOcal Conformal Autoencoder for standardized data coordinates

Randomly Aggregated Least Squares for Support Recovery

The Spectral Underpinning of word2vec

Deep supervised feature selection using Stochastic Gates

Geometry-Based Data Generation

Multi-View Kernel Consensus For Data Analysis and Signal Processing

MultiView Diffusion Maps