High-dimensional asymptotics of prediction: Ridge regression and classification E Dobriban, S Wager The Annals of Statistics, 2015 | 326 | 2015 |
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 | 303 | 2013 |
A Group-Theoretic Framework for Data Augmentation S Chen, E Dobriban, JH Lee NeurIPS 2020 (oral presentation), JMLR, arXiv preprint arXiv:1907.10905, 2019 | 228* | 2019 |
DeltaGrad: Rapid retraining of machine learning models Y Wu, E Dobriban, SB Davidson ICML 2020, arXiv preprint arXiv:2006.14755, 2020 | 189 | 2020 |
Jailbreaking black box large language models in twenty queries P Chao, A Robey, E Dobriban, H Hassani, GJ Pappas, E Wong arXiv preprint arXiv:2310.08419, 2023 | 188 | 2023 |
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 | 150 | 2015 |
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 | 91 | 2020 |
Deterministic parallel analysis: an improved method for selecting factors and principal components E Dobriban, AB Owen Journal of the Royal Statistical Society, Series B, 2017 | 79* | 2017 |
Distributed linear regression by averaging E Dobriban, Y Sheng Annals of Statistics, arXiv preprint arXiv:1810.00412, 2018 | 78 | 2018 |
Dynamic load identification for mechanical systems: A review R Liu, E Dobriban, Z Hou, K Qian Archives of Computational Methods in Engineering 29 (2), 831-863, 2022 | 76 | 2022 |
Asymptotics for sketching in least squares regression E Dobriban, S Liu Neural Information Processing Systems (NeurIPS) 2019, 2018 | 68* | 2018 |
Provable tradeoffs in adversarially robust classification E Dobriban, H Hassani, D Hong, A Robey IEEE Transactions on Information Theory (to appear), https://arxiv.org/abs …, 2020 | 64 | 2020 |
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 | 62* | 2019 |
PCA: high dimensional exponential family PCA LT Liu, E Dobriban, A Singer The Annals of Applied Statistics, 2016 | 61 | 2016 |
Ridge Regression: Structure, Cross-Validation, and Sketching S Liu, E Dobriban International Conference on Learning Representations (ICLR) 2020, arXiv …, 2019 | 60 | 2019 |
Permutation methods for factor analysis and PCA E Dobriban The Annals of Statistics, 2017 | 60* | 2017 |
Efficient computation of limit spectra of sample covariance matrices E Dobriban Random Matrices: Theory and Applications 4 (04), 1550019, 2015 | 47 | 2015 |
What causes the test error? going beyond bias-variance via ANOVA L Lin, E Dobriban Journal of Machine Learning Research 22 (155), 1-82, 2021 | 46 | 2021 |
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection R Kaur, S Jha, A Roy, S Park, E Dobriban, O Sokolsky, I Lee AAAI 2022, arXiv preprint arXiv:2201.02331, 2022 | 44 | 2022 |
How to reduce dimension with PCA and random projections? F Yang, S Liu, E Dobriban, DP Woodruff IEEE Transactions on Information Theory, 2020 | 37 | 2020 |