Boaz Nadler

Publications

Submitted

A. Birnbaum and B. Nadler, High Dimensional Sparse Covariance Estimation: Accurate thresholds for the maximal diagonal entry and for the largest correlation coefficient , 2012.

F. Parisi, F. Strino, B. Nadler, Y. Kluger, The student's dilemma: ranking and improving prediction at test time without access to training data , 2013.

Published

2013

O. Raz, N. Dudovich, B. Nadler, Vectorial Phase Retrieval of 1-D Signals
IEEE Transactions on Signal Processing, 61(7), pp. 1632-1643, 2013.
Matlab code can be found here.

M. Gavish and B. Nadler, Normalized Cuts are approximately inverse exit times,
SIAM Journal of Matrix Analysis, to appear, 2013.

2012

A. Levin, B. Nadler, F. Durand, W. Freeman, Patch Complexity, Finite Pixel Correlations and Optimal Denoising
European Conference Computer Vision (ECCV), 2012.

A. Birnbaum, I.M. Johnstone, B. Nadler and D. Paul, Minimax bounds for sparse PCA with noisy high-dimensional data,
to appear in the Annals of Statistics, 2012.

J. Roeder, B. Nadler, K. Kunzmann and F.A. Hamprecht Active Learning with Distributional Estimates
Conference on Uncertainty in Artificial Intelligence (UAI), 2012.

2011

O. Raz et. al., Vectorial Phase Retrieval for Linear Characterization of Attosecond Pulses
Physical Review Letters, 107, 133902, 2011.

B. Nadler and I.M. Johnstone Detection Performance of Roy's Largest Root Test when the noise covariance matrix is arbitrary,
Statistical Signal Processing Conference, Nice, France, 2011.

For more details and more general results see:
B. Nadler and I. M. Johnstone On the distribution of Roy's Largest Root Test in MANOVA and in Signal Detection in Noise,
Technical Report, Department of Statistics, Stanford University, 2011.

A. Levin and B. Nadler, Natural Image Denoising: Optimality and Inherent Bounds ,
IEEE Conf. on Computer Vision and Pattern Recognition (CVPR-11), 2011.

B. Nadler, On the distribution of the ratio of the largest eigenvalue to the trace of a Wishart matrix , Matlab Code available here
Journal of Multivariate Analysis, Vol. 102(2), 363-371, 2011.

B. Nadler and L. Kontorovich, Model Selection for Sinusoids in Noise: Statistical Analysis and a New Penalty Term ,
IEEE Transactions on Signal Processing, 59(4), 1333-1345, 2011.

B. Nadler, F. Penna and R. Garello, Performance of Eigenvalue-based Signal Detectors with Known and Unknown Noise Level,
IEEE International Conference on Communications ICC2011.

2010

N. Arkind, B. Nadler, Parametric Joint Detection-Estimation of the Number of Sources in Array Processing ,
Sixth IEEE Sensor Array and Multichannel Signal Processing Workshop, 2010.

M. Gavish, B. Nadler, R.R. Coifman, Multiscale wavelets on trees, graphs and high dimensional data: theory and applications to semi supervised learning ,
International Conference on Machine Learning, 2010.
Proof appear in Supplementary Material .

S. Alpert, M. Galun, B. Nadler, R. Basri, Detecting Faint Curved Edges in Noisy Images ,
European Conference Computer Vision (ECCV), 2010.

B. Nadler, Nonparametric Detection of Signals by Information Theoretic Criteria: performance analysis and an improved estimator ,
IEEE Transactions on Signal Processing, 58(5):2746-2756, 2010.

2009

B. Nadler, N. Srebro and X. Zhou, Semi-Supervised Learning with the Graph-Laplacian: The limit of infinite unlabelled data ,
Neural Information Processing Systems (NIPS), Vol. 22, 2009.
S. Kritchman and B. Nadler, Non-parametric Detection of the Number of Signals, Hypothesis Testing and Random Matrix Theory,
IEEE Transactions on Signal Processing, vol. 57, no. 10, pp. 3930--3941, 2009.
B.Nadler, Discussion of "On consistency and sparsity for principal component analysis in high dimensions,
Journal of the American Statistical Association, Vol. 104, No. 486: 694–697, 2009.
B. Andres, U. Köthe, A. Bonea, B. Nadler and Hamprecht F. A., Quantitative Assessment of Image Segmentation Quality by Random Walk Relaxation Times, DAGM Conference, 2009.
L. Kontorovich and B. Nadler, Universal Kernel-Based Learning with Applications to Regular Languages,
Journal of Machine Learning Research, vol. 10, 1095--1129, 2009.
I. Ipsen, B. Nadler, Refined Perturbation Bounds for Eigenvalues of Hermitian and Non-Hermitian Matrices,
SIAM Journal of Matrix Analysis 31(1):40-53 (2009).
A. Singer, Y. Shkolnisky, B. Nadler, Diffusion Interpretation of Non-Local Neighborhood Filters for Signal Denoising,
SIAM Journal on Imaging Sciences, 2(1):118-139, 2009.
I. Drozdov I, M. Kidd, B. Nadler,R.L. Camp RL, SM Mane, O. Hauso, B.I. Gustafsson, I.M. Modlin, Predicting Neuroendocrine Tumor (Carcinoid) Neoplasia Using Gene Expression Profiling and Supervised Machine Learning Cancer, 115(8):1638--1650, 2009.
I.M. Modlin, B.I. Gustafsson, I. Drozdov I, B. Nadler, R. Pfagner, M. Kidd, Principal Component Analysis, Hierarchical Clustering, and Decision Tree Assessment of Plasma mRNA and Hormone Levels as an Early Detection Strategy for Small Intestinal Neuroendocrine (Carcinoid) Tumors, Annals of Surgical Oncology, 16(2):487--498, 2009.

2008

B. Nadler, Finite Sample Approximation Results for principal component analysis: A matrix perturbation approach ,
Annals of Statistics, 36(6):2791--2817, 2008.
S. Kritchman, B. Nadler, Determining the number of components in a factor model from limited noisy data Matlab Code available here
Chemometrics and Intelligent Laboratory Systems, 94:19-32, 2008.
I. Kemelmacher-Shlizerman, R. Basri, and B. Nadler, 3D shape reconstruction of Mooney faces,
IEEE Conf. on computer vision and Pattern Recognition (CVPR-08).
R. Coifman, I. Kevrekidis, S. Lafon, M. Maggioni, B. Nadler, Diffusion maps, reduction coordinates and low dimensional representation of stochastic systems,
SIAM Multiscale modeling and simulation, 7(2):842-864, 2008.
A. Lee, B. Nadler, L. Wasserman, Treelets - An Adaptive Multi-Scale Basis for Sparse Unordered Data,
Annals of Applied Statistics, 2(2):435-471, 2008. Matlab Code available here

2007

B. Nadler, S. Lafon, R. R. Coifman, I. G. Kevrekidis, Diffusion Maps - a probabilistic interpretation for spectral embedding and clustering algorithms, in Principal Manifolds for Data Visualization and Dimension Reduction, A.N. Gorban, B. Kegl, D.C. Wunsch and A. Zinovyev (Eds), Springer, 2007.
R. Erban, T.A. Frewen,X. Wang, T.C. Elston, R. Coifman, B. Nadler, I.G. Kevrekidis,Variable-free exploration of stochastic models: A gene regulatory network example,
Journal of Chemical Physics, 126:155103, 2007.
M. Kidd, B. Nadler, S. Mane, G. Eick, M. Malfertheiner, M. Champaneria, R. Pfragner, I. Modlin, GeneChip, geNorm, and gastrointestinal tumors: novel reference genes for real-time PCR, Physiol. Genomics, 30:363-370, 2007.

2006 and earlier

B. Nadler, M. Galun, Fundamental Limitations of Spectral Clustering Neural Information Processing systems, Vol. 19, 2006.
B. Nadler, S. Lafon, R. R. Coifman, I. G. Kevrekidis, Diffusion Maps, Spectral Clustering and Reaction Coordinates of Dynamical Systems, Applied and Computational Harmonic Analysis, (21):113-127, 2006.
B. Nadler, S. Lafon, R. R. Coifman, I. G. Kevrekidis, Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators, Neural Information Processing Systems (NIPS), Vol 18, 2005.
R. R. Coifman, S. Lafon, A.B. Lee, M. Maggioni,B. Nadler, F. Warner, S. Zucker, Geometric Diffusion as a tool for harmonic analysis and structure definition of data, part I: Diffusion Maps, Proceedings of the National Academy of Sciences, 102(21):7426-31 (2005).
R. R. Coifman, S. Lafon, A.B. Lee, M. Maggioni,B. Nadler, F. Warner, S. Zucker, Geometric Diffusion as a tool for harmonic analysis and structure definition of data, part II: Multiscale methods, Proceedings of the National Academy of Sciences, 102(21):7432-37 (2005).
B. Nadler, R. R. Coifman, The prediction error in CLS and PLS: the importance of feature selection prior to multivariate calibration, preprint of an article published in Journal of Chemometrics, 19(2):107-118 (2005). The final version can be accessed at the publisher's website by clicking here
B. Nadler, R. R. Coifman, Partial least squares, Beer's law and the net analyte signal: statistical modeling and analysis, preprint of an article published in Journal of Chemometrics, 19(1):45-54 (2005). The final version can be accessed at the publisher's website by clicking here

This paper was awarded the Kowalski prize for best theoretical paper in J. Chemometrics in 2004-5.

B. Nadler, Z. Schuss, A. Singer, Langevin Trajectories between fixed concentrations, Physical Review Letters, 94 (2005) 218101.
B. Nadler, Z. Schuss, U. Hollerbach, R.S. Eisenberg, Saturation of conductance in single ion channels: The blocking effect of the near reaction field, Physical Review E, 70 (2004) 051912.
A. Singer, Z. Schuss, B. Nadler, R.S. Eisenberg, Memoryless control of boundary concentrations of diffusing particles, Physical Review E, 70 (2004) 061106.
B. Nadler, Z. Schuss, A. Singer, R.S. Eisenberg, Ionic diffusion through confined geometries: from Langevin equations to partial differential equations, Journal of Physics: Condensed Matter, 16(22) (2004) S2153-S2165.
B. Nadler, U. Hollerbach and R.S. Eisenberg, Dielectric boundary force and its crucial role in gramicidin, Physical Review E, 68 (2003) 021905.
Z. Schuss, B. Nadler, R.S. Eisenberg, Derivation of Poisson and Nernst-Planck equations in a bath and channel from a molecular model, Physical Review E, 64 (2001) 036116.
B. Nadler, T. Naeh, Z. Schuss, Connecting a Discrete Ionic Simulation to a Continuum, SIAM Journal on Applied Mathematics, 63, Number 3 (2003), pp. 850-873.
B. Nadler, T. Naeh, Z. Schuss, The Stationary Arrival Process of Independent Diffusers from a Continuum to an Absorbing Boundary Is Poissonian, SIAM Journal on Applied Mathematics, 62(2) (2001), pp. 433-447.
B. Nadler, G. Fibich, S. Lev-Yehudi, Daniel Cohen-Or, A qualitative and quantitative visibility analysis in urban scenes, Computers and Graphics, 23(5):655-666 (1999).