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D-Index & Metrics

Computer Science

D-Index
40
Citations
10570
World Ranking
9097
National Ranking
147

Overview

Boaz Nadler is affiliated with the Weizmann Institute of Science in Israel. Their research spans multiple fields, primarily in Computer Science and Engineering. The scientist's body of work includes significant contributions to computational mechanics, artificial intelligence, molecular biology, statistics and probability, and signal processing.

The main research topics covered by Boaz Nadler involve sparse and compressive sensing techniques, machine learning and algorithms, Bayesian methods and mixture models, statistical methods and inference, blind source separation techniques, image and signal denoising methods, and genomics and phylogenetic studies.

Boaz Nadler has published extensively in various venues. Frequent publication outlets include:

  • arXiv (Cornell University)
  • SIAM Journal on Mathematics of Data Science
  • Information and Inference A Journal of the IMA
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Nature Communications

Notable recent papers are:

  • Zero-preserving imputation of single-cell RNA-seq data, 2022, Nature Communications
  • Rank 2r Iterative Least Squares: Efficient Recovery of Ill-Conditioned Low Rank Matrices from Few Entries, 2021, SIAM Journal on Mathematics of Data Science
  • GNMR: A Provable One-Line Algorithm for Low Rank Matrix Recovery, 2022, SIAM Journal on Mathematics of Data Science
  • "Self-Wiener" Filtering: Data-Driven Deconvolution of Deterministic Signals, 2021, IEEE Transactions on Signal Processing
  • Tight recovery guarantees for orthogonal matching pursuit under Gaussian noise, 2020, Information and Inference A Journal of the IMA

Collaboration has been a significant aspect of their work. Frequent co-authors include:

  • Pini Zilber
  • Yuval Kluger
  • Chen Amiraz
  • Robert Krauthgamer
  • Ariel Jaffe

Best Publications

  • Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps

    R. R. Coifman;S. Lafon;A. B. Lee;M. Maggioni

  • Diffusion maps, spectral clustering and reaction coordinates of dynamical systems

    Boaz Nadler;Stéphane Lafon;Ronald R. Coifman;Ioannis G. Kevrekidis

  • Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators

    Boaz Nadler;Stephane Lafon;Ioannis Kevrekidis;Ronald R. Coifman

  • Finite sample approximation results for principal component analysis: A matrix perturbation approach

    Boaz Nadler

  • Non-Parametric Detection of the Number of Signals: Hypothesis Testing and Random Matrix Theory

    S. Kritchman;B. Nadler

  • Natural image denoising: Optimality and inherent bounds

    Anat Levin;Boaz Nadler

  • Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods

    R. R. Coifman;S. Lafon;A. B. Lee;M. Maggioni

  • Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi Supervised Learning

    Matan Gavish;Boaz Nadler;Ronald R. Coifman

  • Determining the number of components in a factor model from limited noisy data

    Shira Kritchman;Boaz Nadler

  • SpectralNet: Spectral Clustering using Deep Neural Networks

    Uri Shaham;Kelly P. Stanton;Henry Li;Boaz Nadler

  • Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck operators

    Boaz Nadler;Stephane Lafon;Ronald R. Coifman;Ioannis G. Kevrekidis

  • Nonparametric Detection of Signals by Information Theoretic Criteria: Performance Analysis and an Improved Estimator

    Boaz Nadler

  • Accurate Blur Models vs. Image Priors in Single Image Super-resolution

    Netalee Efrat;Daniel Glasner;Alexander Apartsin;Boaz Nadler

  • Performance of Eigenvalue-Based Signal Detectors with Known and Unknown Noise Level

    Boaz Nadler;Federico Penna;Roberto Garello

  • Fundamental Limitations of Spectral Clustering

    Boaz Nadler;Meirav Galun

  • Minimax bounds for sparse PCA with noisy high-dimensional data

    Aharon Birnbaum;Iain M. Johnstone;Boaz Nadler;Debashis Paul

  • The prediction error in CLS and PLS : the importance of feature selection prior to multivariate calibration

    Boaz Nadler;Ronald R. Coifman

  • Patch complexity, finite pixel correlations and optimal denoising

    Anat Levin;Boaz Nadler;Fredo Durand;William T. Freeman

  • Ranking and combining multiple predictors without labeled data.

    Fabio Parisi;Francesco Strino;Boaz Nadler;Yuval Kluger;Yuval Kluger

  • On the optimality of averaging in distributed statistical learning

    Jonathan D. Rosenblatt;Boaz Nadler

  • Treelets--An adaptive multi-scale basis for sparse unordered data

    Ann B. Lee;Boaz Nadler;Larry Wasserman

Frequent Co-Authors

Yuval Kluger
Yuval Kluger Yale University
Ronald R. Coifman
Ronald R. Coifman Yale University
Ronen Basri
Ronen Basri Weizmann Institute of Science
Amit Singer
Amit Singer Princeton University
Robert Krauthgamer
Robert Krauthgamer Weizmann Institute of Science
Fred A. Hamprecht
Fred A. Hamprecht Heidelberg University
Larry Wasserman
Larry Wasserman Carnegie Mellon University
Anat Levin
Anat Levin Technion – Israel Institute of Technology
Shrikant Mane
Shrikant Mane Yale University
Mauro Maggioni
Mauro Maggioni Johns Hopkins University

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