Edgar Dobriban

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Hamed HassaniUniversity of Pennsylvania (EE, CS, Stat)Email verificata su seas.upenn.edu
Insup LeeProfessor, Computer and Information Science, University of PennsylvaniaEmail verificata su cis.upenn.edu
Patrick ChaoUniversity of PennsylvaniaEmail verificata su wharton.upenn.edu
Sifan LiuFlatiron InstituteEmail verificata su flatironinstitute.org
Alex RobeyPostdoc, Carnegie Mellon UniversityEmail verificata su andrew.cmu.edu
Yue ShengUniversity of PennsylvaniaEmail verificata su sas.upenn.edu
Art OwenProfessor of Statistics, Stanford UniversityEmail verificata su stanford.edu
Osbert BastaniUniversity of PennsylvaniaEmail verificata su seas.upenn.edu
George PappasUPS Foundation Professor, Electrical and Systems Engineering, University of PennsylvaniaEmail verificata su seas.upenn.edu
Susan DavidsonUniversity of PennsylvaniaEmail verificata su cis.upenn.edu
Xinmeng HuangPhD, University of PennsylvaniaEmail verificata su sas.upenn.edu
Donghwan LeeUniversity of PennsylvaniaEmail verificata su sas.upenn.edu
Stefan WagerGraduate School of Business, Stanford UniversityEmail verificata su stanford.edu
Sangdon ParkPOSTECH GSAI/CSEEmail verificata su postech.ac.kr
David HongAssistant Professor, Department of Electrical and Computer Engineering, University of DelawareEmail verificata su udel.edu
Amit SingerPrinceton UniversityEmail verificata su math.princeton.edu
Shuxiao ChenUniversity of PennsylvaniaEmail verificata su wharton.upenn.edu
Jane H. LeeYale UniversityEmail verificata su yale.edu
Kristen FortneyBioAge Labs, Stanford University, University of TorontoEmail verificata su bioagelabs.com
Kostas DaniilidisRuth Yalom Stone Professor of Computer and Information Science, University of PennsylvaniaEmail verificata su cis.upenn.edu

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Edgar Dobriban

Statistics & Computer Science, University of Pennsylvania

Email verificata su upenn.edu - Home page

Statistics Machine Learning


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Jailbreaking black box large language models in twenty queries P Chao, A Robey, E Dobriban, H Hassani, GJ Pappas, E Wong IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) 2025 …, 2023	439	2023
High-dimensional asymptotics of prediction: Ridge regression and classification E Dobriban, S Wager The Annals of Statistics, 2015	376	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	312	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	264*	2019
DeltaGrad: Rapid retraining of machine learning models Y Wu, E Dobriban, SB Davidson ICML 2020, arXiv preprint arXiv:2006.14755, 2020	236	2020
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	162	2015
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	112	2022
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	103	2020
Jailbreakbench: An open robustness benchmark for jailbreaking large language models P Chao, E Debenedetti, A Robey, M Andriushchenko, F Croce, V Sehwag, ... NeurIPS 2024, Datasets and Benchmarks Track, 2024	95	2024
Distributed linear regression by averaging E Dobriban, Y Sheng Annals of Statistics, arXiv preprint arXiv:1810.00412, 2018	87	2018
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	87	2017
Asymptotics for sketching in least squares regression E Dobriban, S Liu Neural Information Processing Systems (NeurIPS) 2019, 2018	74*	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	73	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	70*	2019
Permutation methods for factor analysis and PCA E Dobriban The Annals of Statistics, 2017	67*	2017
Ridge Regression: Structure, Cross-Validation, and Sketching S Liu, E Dobriban International Conference on Learning Representations (ICLR) 2020, arXiv …, 2019	66	2019
PCA: high dimensional exponential family PCA LT Liu, E Dobriban, A Singer The Annals of Applied Statistics, 2016	61	2016
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	54	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	53	2022
Efficient computation of limit spectra of sample covariance matrices E Dobriban Random Matrices: Theory and Applications 4 (04), 1550019, 2015	48	2015

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