Edgar Dobriban
Edgar Dobriban
Statistics & Computer Science, University of Pennsylvania
Verified email at upenn.edu - Homepage
Title
Cited by
Cited by
Year
Certifying the restricted isometry property is hard
AS Bandeira, E Dobriban, DG Mixon, WF Sawin
IEEE transactions on information theory 59 (6), 3448-3450, 2013
2352013
High-dimensional asymptotics of prediction: Ridge regression and classification
E Dobriban, S Wager
The Annals of Statistics, 2015
1262015
Genome-wide scan informed by age-related disease identifies loci for exceptional human longevity
K Fortney, E Dobriban, P Garagnani, C Pirazzini, D Monti, D Mari, ...
PLoS genetics 11 (12), e1005728, 2015
1142015
Deterministic parallel analysis: an improved method for selecting factors and principal components
E Dobriban, AB Owen
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2019
43*2019
PCA: high dimensional exponential family PCA
LT Liu, E Dobriban, A Singer
The Annals of Applied Statistics, 2016
402016
A Group-Theoretic Framework for Data Augmentation
S Chen, E Dobriban, JH Lee
NeurIPS 2020 (oral presentation), JMLR, arXiv preprint arXiv:1907.10905, 2019
37*2019
Permutation methods for factor analysis and PCA
E Dobriban
The Annals of Statistics, 2017
35*2017
Efficient computation of limit spectra of sample covariance matrices
E Dobriban
Random Matrices: Theory and Applications 4 (04), 1550019, 2015
342015
Asymptotics for sketching in least squares regression
E Dobriban, S Liu
Neural Information Processing Systems (NeurIPS) 2019, 2018
29*2018
Optimal multiple testing under a Gaussian prior on the effect sizes
E Dobriban, K Fortney, SK Kim, AB Owen
Biometrika 102 (4), 753-766, 2015
292015
Optimal prediction in the linearly transformed spiked model
E Dobriban, W Leeb, A Singer
The Annals of Statistics, 2017
28*2017
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
A Ali, E Dobriban, RJ Tibshirani
International Conference on Machine Learning (ICML) 2020, https://arxiv.org …, 2020
272020
Sharp detection in PCA under correlations: all eigenvalues matter
E Dobriban
The Annals of Statistics 45 (4), 1810-1833, 2017
212017
Ridge Regression: Structure, Cross-Validation, and Sketching
S Liu, E Dobriban
International Conference on Learning Representations (ICLR) 2020, arXiv …, 2019
202019
Distributed linear regression by averaging
E Dobriban, Y Sheng
Annals of Statistics, arXiv preprint arXiv:1810.00412, 2018
202018
WONDER: Weighted one-shot distributed ridge regression in high dimensions
E Dobriban, Y Sheng
ICML 2020, Journal of Machine Learning Research (JMLR), arXiv preprint arXiv …, 2019
16*2019
DeltaGrad: Rapid retraining of machine learning models
Y Wu, E Dobriban, SB Davidson
ICML 2020, arXiv preprint arXiv:2006.14755, 2020
152020
Provable tradeoffs in adversarially robust classification
E Dobriban, H Hassani, D Hong, A Robey
https://arxiv.org/abs/2006.05161, 2020
142020
How to reduce dimension with PCA and random projections?
F Yang, S Liu, E Dobriban, DP Woodruff
IEEE Transactions on Information Theory (to appear), arXiv preprint arXiv …, 2020
122020
Limiting spectrum of randomized hadamard transform and optimal iterative sketching methods
J Lacotte, S Liu, E Dobriban, M Pilanci
NeurIPS 2020, arXiv preprint arXiv:2002.00864, 2020
11*2020
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