| Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps RR Coifman, S Lafon, AB Lee, M Maggioni, B Nadler, F Warner, ... Proceedings of the national academy of sciences 102 (21), 7426-7431, 2005 | 1477 | 2005 |
| Diffusion maps, spectral clustering and reaction coordinates of dynamical systems B Nadler, S Lafon, RR Coifman, IG Kevrekidis Applied and Computational Harmonic Analysis 21 (1), 113-127, 2006 | 553 | 2006 |
| Diffusion maps, spectral clustering and eigenfunctions of Fokker-Planck operators B Nadler, S Lafon, RR Coifman, IG Kevrekidis Neural Information Processing Systems, 2005 | 517 | 2005 |
| Non-parametric detection of the number of signals: Hypothesis testing and random matrix theory S Kritchman, B Nadler IEEE Transactions on Signal Processing 57 (10), 3930-3941, 2009 | 294 | 2009 |
| Finite sample approximation results for principal component analysis: A matrix perturbation approach B Nadler The Annals of Statistics 36 (6), 2791-2817, 2008 | 277 | 2008 |
| Natural image denoising: Optimality and inherent bounds A Levin, B Nadler CVPR 2011, 2833-2840, 2011 | 259 | 2011 |
| Derivation of Poisson and Nernst-Planck equations in a bath and channel from a molecular model Z Schuss, B Nadler, RS Eisenberg Physical Review E 64 (3), 036116, 2001 | 254* | 2001 |
| Diffusion maps, reduction coordinates, and low dimensional representation of stochastic systems RR Coifman, IG Kevrekidis, S Lafon, M Maggioni, B Nadler Multiscale Modeling & Simulation 7 (2), 842-864, 2008 | 236 | 2008 |
| Multiscale wavelets on trees, graphs and high dimensional data: Theory and applications to semi supervised learning M Gavish, B Nadler, RR Coifman ICML, 2010 | 230 | 2010 |
| Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods RR Coifman, S Lafon, AB Lee, M Maggioni, B Nadler, F Warner, ... Proceedings of the National Academy of Sciences of the United States of …, 2005 | 219 | 2005 |
| Determining the number of components in a factor model from limited noisy data S Kritchman, B Nadler Chemometrics and Intelligent Laboratory Systems 94 (1), 19-32, 2008 | 217 | 2008 |
| Nonparametric detection of signals by information theoretic criteria: performance analysis and an improved estimator B Nadler IEEE Transactions on Signal Processing 58 (5), 2746-2756, 2010 | 186 | 2010 |
| Fundamental limitations of spectral clustering B Nadler, M Galun Advances in neural information processing systems 19, 1017-1024, 2006 | 171 | 2006 |
| Performance of eigenvalue-based signal detectors with known and unknown noise level B Nadler, F Penna, R Garello 2011 IEEE International Conference on Communications (ICC), 1-5, 2011 | 169 | 2011 |
| Discussion of: Treelets—an adaptive multi-scale basis for sparse unordered data C Tuglus, MJ van der Laan The Annals of Applied Statistics 2 (2), 489, 2008 | 161 | 2008 |
| Statistical analysis of semi-supervised learning: The limit of infinite unlabelled data B Nadler, N Srebro, X Zhou NIPS, 2009 | 153* | 2009 |
| Minimax bounds for sparse PCA with noisy high-dimensional data A Birnbaum, IM Johnstone, B Nadler, D Paul Annals of statistics 41 (3), 1055, 2013 | 147 | 2013 |
| Patch complexity, finite pixel correlations and optimal denoising A Levin, B Nadler, F Durand, WT Freeman European Conference on Computer Vision, 73-86, 2012 | 143 | 2012 |
| SpectralNet: Spectral Clustering using Deep Neural Networks U Shaham, K Stanton, H Li, B Nadler, R Basri, Y Kluger International Conference on Learning Representations, 2018 | 139 | 2018 |
| Accurate blur models vs. image priors in single image super-resolution N Efrat, D Glasner, A Apartsin, B Nadler, A Levin Proceedings of the IEEE International Conference on Computer Vision, 2832-2839, 2013 | 127 | 2013 |
Ronald Coifmanprofessor of mathematics yale universityVerified email at math.yale.edu
