2.1k citations · 2.7k across the 14 of their papers we have counts for
15 papers
Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features
Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky +1
Modern datasets often contain large subsets of correlated features and nuisance features, which are not or loosely related to the main underlying structures of the data. Nuisance f…
Discovery of Single Independent Latent Variable
Uri Shaham, Jonathan Svirsky, Ori Katz +1
Latent variable discovery is a central problem in data analysis with a broad range of applications in applied science. In this work, we consider data given as an invertible mixture…
Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities
Uri Shaham, Igal Zaidman, Jonathan Svirsky
It is often desired that ordinal regression models yield unimodal predictions. However, in many recent works this characteristic is either absent, or implemented using soft targets…
Differentiable Unsupervised Feature Selection based on a Gated Laplacian
Ofir Lindenbaum, Uri Shaham, Jonathan Svirsky +2
Scientific observations may consist of a large number of variables (features). Identifying a subset of meaningful features is often ignored in unsupervised learning, despite its po…
Learning to Ask Medical Questions using Reinforcement Learning
Uri Shaham, Tom Zahavy, Cesar Caraballo +3
We propose a novel reinforcement learning-based approach for adaptive and iterative feature selection. Given a masked vector of input features, a reinforcement learning agent itera…
Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks
Benjamin Au, Uri Shaham, Sanket Dhruva +7
The determination of a coronary stenosis and its severity in current clinical workflow is typically accomplished manually via physician visual assessment (PVA) during invasive coro…
Defending against Adversarial Images using Basis Functions Transformations
Uri Shaham, James Garritano, Yutaro Yamada +5
We study the effectiveness of various approaches that defend against adversarial attacks on deep networks via manipulations based on basis function representations of images. Speci…
SpectralNet: Spectral Clustering using Deep Neural Networks
Uri Shaham, Kelly Stanton, Henry Li +3
Spectral clustering is a leading and popular technique in unsupervised data analysis. Two of its major limitations are scalability and generalization of the spectral embedding (i.e…
Stochastic Neighbor Embedding separates well-separated clusters
Uri Shaham, Stefan Steinerberger
Stochastic Neighbor Embedding and its variants are widely used dimensionality reduction techniques -- despite their popularity, no theoretical results are known. We prove that the…
Removal of Batch Effects using Distribution-Matching Residual Networks
Uri Shaham, Kelly P. Stanton, Jun Zhao +4
Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of system…
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
Jared Katzman, Uri Shaham, Jonathan Bates +3
Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of var…
A Deep Learning Approach to Unsupervised Ensemble Learning
Uri Shaham, Xiuyuan Cheng, Omer Dror +4
We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning. First, we prove that the popular model of Dawid and Skene, whic…
Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks
Uri Shaham, Roy Lederman
We consider the statistical problem of learning common source of variability in data which are synchronously captured by multiple sensors, and demonstrate that Siamese neural netwo…
Provable approximation properties for deep neural networks
Uri Shaham, Alexander Cloninger, Ronald R. Coifman
We discuss approximation of functions using deep neural nets. Given a function on a -dimensional manifold , we construct a sparsely-connected depth-4…
Diffusion Nets
Gal Mishne, Uri Shaham, Alexander Cloninger +1
Non-linear manifold learning enables high-dimensional data analysis, but requires out-of-sample-extension methods to process new data points. In this paper, we propose a manifold l…