b'Yuval Kluger'

research

∙ 06/20/2023

The Dyson Equalizer: Adaptive Noise Stabilization for Low-Rank Signal Detection and Recovery

Detecting and recovering a low-rank signal in a noisy data matrix is a f...

0 Boris Landa, et al. ∙

research

∙ 03/16/2023

Multi-modal Differentiable Unsupervised Feature Selection

Multi-modal high throughput biological data presents a great scientific ...

0 Junchen Yang, et al. ∙

research

∙ 10/19/2022

Autoregressive Generative Modeling with Noise Conditional Maximum Likelihood Estimation

We introduce a simple modification to the standard maximum likelihood es...

0 Henry Li, et al. ∙

research

∙ 07/18/2022

ManiFeSt: Manifold-based Feature Selection for Small Data Sets

In this paper, we present a new method for few-sample supervised feature...

0 David Cohen, et al. ∙

research

∙ 06/22/2022

Neural Inverse Transform Sampler

Any explicit functional representation f of a density is hampered by two...

0 Henry Li, et al. ∙

research

∙ 10/11/2021

Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features

Modern datasets often contain large subsets of correlated features and n...

0 Uri Shaham, et al. ∙

research

∙ 10/01/2021

Probabilistic Robust Autoencoders for Anomaly Detection

Empirical observations often consist of anomalies (or outliers) that con...

0 Yariv Aizenbud, et al. ∙

research

∙ 06/11/2021

Locally Sparse Networks for Interpretable Predictions

Despite the enormous success of neural networks, they are still hard to ...

0 Junchen Yang, et al. ∙

research

∙ 04/25/2021

On the Efficient Evaluation of the Azimuthal Fourier Components of the Green's Function for Helmholtz's Equation in Cylindrical Coordinates

In this manuscript, we develop an efficient algorithm to evaluate the az...

0 James Garritano, et al. ∙

research

∙ 03/25/2021

Biwhitening Reveals the Rank of a Count Matrix

Estimating the rank of a corrupted data matrix is an important task in d...

0 Boris Landa, 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

∙ 11/06/2020

Local Two-Sample Testing over Graphs and Point-Clouds by Random-Walk Distributions

Two-sample testing is a fundamental tool for scientific discovery. Yet, ...

0 Boris Landa, et al. ∙

research

∙ 10/12/2020

Deep Gated Canonical Correlation Analysis

Canonical Correlation Analysis (CCA) models can extract informative corr...

5 Ofir Lindenbaum, et al. ∙

research

∙ 07/09/2020

Let the Data Choose its Features: Differentiable Unsupervised Feature Selection

Scientific observations often consist of a large number of variables (fe...

7 Ofir Lindenbaum, et al. ∙

research

∙ 05/31/2020

Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to Heteroskedastic Noise

A fundamental step in many data-analysis techniques is the construction ...

2 Boris Landa, 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

∙ 02/27/2020

The Spectral Underpinning of word2vec

word2vec due to Mikolov et al. (2013) is a word embedding method that is...

0 Ariel Jaffe, et al. ∙

research

∙ 02/15/2019

Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations

T-distributed stochastic neighbour embedding (t-SNE) is a widely used da...

0 Dmitry Kobak, et al. ∙

research

∙ 10/09/2018

Deep supervised feature selection using Stochastic Gates

In this study, we propose a novel non-parametric embedded feature select...

0 Yutaro Yamada, et al. ∙

research

∙ 03/28/2018

Defending against Adversarial Images using Basis Functions Transformations

We study the effectiveness of various approaches that defend against adv...

0 Uri Shaham, 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

∙ 12/25/2017

Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding

t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for ...

0 George C. Linderman, et al. ∙

research

∙ 11/13/2017

Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science

If we pick n random points uniformly in [0,1]^d and connect each point t...

0 George C. Linderman, et al. ∙

research

∙ 08/18/2017

Data-Driven Tree Transforms and Metrics

We consider the analysis of high dimensional data given in the form of a...

0 Gal Mishne, et al. ∙

research

∙ 08/13/2017

Mahalanonbis Distance Informed by Clustering

A fundamental question in data analysis, machine learning and signal pro...

0 Almog Lahav, 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

∙ 10/13/2016

Removal of Batch Effects using Distribution-Matching Residual Networks

Sources of variability in experimentally derived data include measuremen...

0 Uri Shaham, et al. ∙

research

∙ 06/02/2016

DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network

Medical practitioners use survival models to explore and understand the ...

0 Jared Katzman, 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

∙ 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

∙ 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. ∙

Yuval Kluger

Featured Co-authors