Publications
Submitted
B. Leshem, O. Raz, A. Jaffe and B. Nadler,
The discrete sign problem: uniqueness, recovery algorithms and phase retrieval applications
, 2016.
U. Shaham, X. Cheng, O. Dror, A. Jaffe, B. Nadler, J. Chang and Y. Kluger
A deep learning approach to unsupervised ensemble learning
, to appear in ICML, 2016.
N. Ofir, M. Galun, B. Nadler and R. Basri
Fast Detection of Curved Edges at Low SNR
, to appear in CVPR, 2016.
J. Rosenblatt, B. Nadler,
On the Optimality of Averaging in Distributed Statistical Learning
, to appear in Information and Inference, 2016.
A. MoscovichEiger, B. Nadler,
Fast calculation of boundary crossing probabilities for Poisson processes
, 2015. Corresponding code available
here
I.M. Johnstone, B. Nadler,
Roy's Largest Root Test Under RankOne Alternatives
, 2015.
A. MoscovichEiger, B. Nadler and C. Spiegelman,
On the exact BerkJones statistics and their pvalue calculation
, 2015.
For more information, code, examples etc. see here
A. Birnbaum and B. Nadler,
High Dimensional Sparse Covariance Estimation: Accurate thresholds for the maximal diagonal entry and for the
largest correlation coefficient
, 2012.
Published
2016

B. Leshem, R. Xu, Y. Dalal, J. Miao, B. Nadler, D. Oron, N. Dudovich and O. Raz,
Direct singleshot phase retrieval from the diffraction pattern of separated objects
,
Nature Communications, vol. 7, article no. 10820, 2016.
2015

I. Horev, B. Nadler, E. AriasCastro, M. Galun and R. Basri,
Detection of Long Edges on a Computational Budget: A Sublinear Approach
,
SIAM Journal of Imaging Sciences, vol. 8, no. 1., pp. 458483, 2015.
Matlab/C Code available here.

A. Jaffe, B. Nadler, Y. Kluger,
Estimating the accuracies of multiple classifiers without labeled data
,
AISTATS2015. For more information and code, click here.
2014

O. Raz, B. Leshem, J. Miao, B. Nadler, D. Oron, N. Dudovich
Direct Phase Retrieval in double blind Fourier Holography
Optics Express, vol. 22, 2493524950, 2014.

F. Parisi, F. Strino, B. Nadler, Y. Kluger,
Ranking and Combining Multiple predictors without labeled data
Proceedings of the National Academy of Sciences, vol. 111(4), 12531258, 2014. For more information and code, click here.
2013
 A. Kontorovich, B. Nadler and R. Weiss,
On learning parametricoutput HMMs
,
ICML, 2013.

A. Birnbaum, I.M. Johnstone, B. Nadler and D. Paul,
Minimax bounds for sparse PCA with noisy highdimensional data
the Annals of Statistics, 41(3):10551084, 2013.
 O. Raz, N. Dudovich, B. Nadler,
Vectorial Phase Retrieval of 1D Signals
IEEE Transactions on Signal Processing, 61(7), pp. 16321643, 2013.
Matlab code can be found here.
 N. Efrat, D. Glasner, A. Apartsin, B. Nadler, A. Levin.
Accurate Blur Models vs. Image Priors in Single Image SuperResolution
ICCV, 2013.

M. Gavish and B. Nadler,
Normalized Cuts are approximately inverse exit times,
SIAM Journal of Matrix Analysis, 34(2), 757772, 2013.
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 (CVPR11), 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), 363371,
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), 13331345, 2011.

B. Nadler, F. Penna and R. Garello,
Performance of Eigenvaluebased Signal Detectors with Known and Unknown Noise Level,
IEEE International Conference on Communications ICC2011.
2010

N. Arkind, B. Nadler,
Parametric Joint DetectionEstimation 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):27462756, 2010.
2009

B. Nadler, N. Srebro and X. Zhou,
SemiSupervised Learning with the GraphLaplacian:
The limit of infinite unlabelled data ,
Neural Information Processing Systems (NIPS), Vol. 22, 2009.

S. Kritchman and B. Nadler,
Nonparametric Detection of the
Number of Signals, Hypothesis Testing and Random Matrix Theory,
IEEE Transactions on Signal Processing, vol. 57, no. 10, pp. 39303941, 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 KernelBased Learning with Applications to Regular Languages,
Journal of Machine Learning Research, vol. 10, 10951129, 2009.

I. Ipsen, B. Nadler,
Refined Perturbation Bounds for Eigenvalues of Hermitian and NonHermitian
Matrices,
SIAM Journal of Matrix Analysis 31(1):4053 (2009).
 A. Singer, Y. Shkolnisky, B. Nadler,
Diffusion Interpretation of NonLocal Neighborhood Filters for Signal
Denoising,
SIAM Journal on Imaging Sciences, 2(1):118139, 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):16381650, 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):487498, 2009.
2008
 B. Nadler,
Finite Sample Approximation Results for principal component analysis: A matrix perturbation approach
,
Annals of Statistics,
36(6):27912817, 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, 94:1932, 2008.
 I. KemelmacherShlizerman, R. Basri, and B. Nadler,
3D shape reconstruction of Mooney faces,
IEEE Conf. on computer vision and Pattern Recognition (CVPR08).
 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):842864, 2008.
 A. Lee, B. Nadler, L. Wasserman,
Treelets  An Adaptive MultiScale Basis for Sparse Unordered Data,
Annals of Applied Statistics,
2(2):435471, 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,
Variablefree 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 realtime PCR,
Physiol. Genomics, 30:363370, 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):113127, 2006.
 B. Nadler, S. Lafon, R. R. Coifman, I. G. Kevrekidis,
Diffusion
Maps, Spectral Clustering and Eigenfunctions of FokkerPlanck
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):742631 (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):743237 (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):107118
(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):4554 (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 20045.
 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) S2153S2165.
 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 NernstPlanck 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. 850873.
 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. 433447.
 B. Nadler, G. Fibich, S. LevYehudi, Daniel CohenOr,
A
qualitative and quantitative visibility analysis in urban scenes,
Computers and Graphics, 23(5):655666 (1999).