Edgar Dobriban

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 68

Top Topics

Normalization Method

Risk Minimization

Ridge Regression

Top Venues

Publications

Xinmeng Huang, Shuo Li, Mengxin Yu, Matteo Sesia, Hamed Hassani, Insup Lee, Osbert Bastani, Edgar Dobriban
Uncertainty in Language Models: Assessment through Rank-Calibration. CoRR (2024)
Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramèr, Hamed Hassani, Eric Wong
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. CoRR (2024)
Patrick Chao, Edgar Dobriban, Hamed Hassani
Watermarking Language Models with Error Correcting Codes. CoRR (2024)
Behrad Moniri, Hamed Hassani, Edgar Dobriban
Evaluating the Performance of Large Language Models via Debates. CoRR (2024)
Wenwen Si, Sangdon Park, Insup Lee, Edgar Dobriban, Osbert Bastani
PAC Prediction Sets Under Label Shift. ICLR (2024)
Xinmeng Huang, Shuo Li, Edgar Dobriban, Osbert Bastani, Hamed Hassani, Dongsheng Ding
One-Shot Safety Alignment for Large Language Models via Optimal Dualization. CoRR (2024)
Xianli Zeng, Guang Cheng, Edgar Dobriban
Minimax Optimal Fair Classification with Bounded Demographic Disparity. CoRR (2024)
Xianli Zeng, Guang Cheng, Edgar Dobriban
Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing. CoRR (2024)
Edgar Dobriban, Mengxin Yu
SymmPI: Predictive Inference for Data with Group Symmetries. CoRR (2023)
Druv Pai, Michael Psenka, Chih-Yuan Chiu, Manxi Wu, Edgar Dobriban, Yi Ma
Pursuit of a discriminative representation for multiple subspaces via sequential games. J. Frankl. Inst. 360 (6) (2023)
Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani
Demystifying Disagreement-on-the-Line in High Dimensions. CoRR (2023)
Yahan Yang, Sunghye Cho, Maxine Covello, Azia Knox, Osbert Bastani, James Weimer, Edgar Dobriban, Robert T. Schultz, Insup Lee, Julia Parish-Morris
Automatically Predicting Perceived Conversation Quality in a Pediatric Sample Enriched for Autism. INTERSPEECH (2023)
Wenwen Si, Sangdon Park, Insup Lee, Edgar Dobriban, Osbert Bastani
PAC Prediction Sets Under Label Shift. CoRR (2023)
Edgar Dobriban, Hamed Hassani, David Hong, Alexander Robey
Provable Tradeoffs in Adversarially Robust Classification. IEEE Trans. Inf. Theory 69 (12) (2023)
Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Kostas Daniilidis, Edgar Dobriban
Learning Augmentation Distributions using Transformed Risk Minimization. Trans. Mach. Learn. Res. 2023 (2023)
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong
Jailbreaking Black Box Large Language Models in Twenty Queries. CoRR (2023)
Behrad Moniri, Donghwan Lee, Hamed Hassani, Edgar Dobriban
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks. CoRR (2023)
Patrick Chao, Edgar Dobriban
Statistical Estimation Under Distribution Shift: Wasserstein Perturbations and Minimax Theory. CoRR (2023)
Xinmeng Huang, Kan Xu, Donghwan Lee, Hamed Hassani, Hamsa Bastani, Edgar Dobriban
Optimal Heterogeneous Collaborative Linear Regression and Contextual Bandits. CoRR (2023)
Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban, Kostas Daniilidis
SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space. ICLR (2023)
Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani
Demystifying Disagreement-on-the-Line in High Dimensions. ICML (2023)
Yinshuang Xu, Jiahui Lei, Edgar Dobriban, Kostas Daniilidis
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces. CoRR (2022)
Druv Pai, Michael Psenka, Chih-Yuan Chiu, Manxi Wu, Edgar Dobriban, Yi Ma
Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games. CoRR (2022)
Souradeep Dutta, Yahan Yang, Elena Bernardis, Edgar Dobriban, Insup Lee
Memory Classifiers: Two-stage Classification for Robustness in Machine Learning. CoRR (2022)
Xinmeng Huang, Donghwan Lee, Edgar Dobriban, Hamed Hassani
Collaborative Learning of Distributions under Heterogeneity and Communication Constraints. CoRR (2022)
Xinmeng Huang, Donghwan Lee, Edgar Dobriban, Hamed Hassani
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints. NeurIPS (2022)
Xianli Zeng, Edgar Dobriban, Guang Cheng
Fair Bayes-Optimal Classifiers Under Predictive Parity. CoRR (2022)
Ramneet Kaur, Susmit Jha, Anirban Roy, Sangdon Park, Edgar Dobriban, Oleg Sokolsky, Insup Lee
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection. CoRR (2022)
Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani
PAC Prediction Sets for Meta-Learning. NeurIPS (2022)
Souradeep Dutta, Kaustubh Sridhar, Osbert Bastani, Edgar Dobriban, James Weimer, Insup Lee, Julia Parish-Morris
Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates. CoRL (2022)
Souradeep Dutta, Kaustubh Sridhar, Osbert Bastani, Edgar Dobriban, James Weimer, Insup Lee, Julia Parish-Morris
Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates. CoRR (2022)
Xianli Zeng, Edgar Dobriban, Guang Cheng
Fair Bayes-Optimal Classifiers Under Predictive Parity. NeurIPS (2022)
Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban, Kostas Daniilidis
SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space. CoRR (2022)
Ramneet Kaur, Susmit Jha, Anirban Roy, Sangdon Park, Edgar Dobriban, Oleg Sokolsky, Insup Lee
iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection. AAAI (2022)
Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani
PAC Prediction Sets Under Covariate Shift. ICLR (2022)
Shuo Li, Xiayan Ji, Edgar Dobriban, Oleg Sokolsky, Insup Lee
PAC-Wrap: Semi-Supervised PAC Anomaly Detection. KDD (2022)
Xianli Zeng, Edgar Dobriban, Guang Cheng
Bayes-Optimal Classifiers under Group Fairness. CoRR (2022)
Donghwan Lee, Xinmeng Huang, Hamed Hassani, Edgar Dobriban
T-Cal: An optimal test for the calibration of predictive models. CoRR (2022)
Shuo Li, Xiayan Ji, Edgar Dobriban, Oleg Sokolsky, Insup Lee
PAC-Wrap: Semi-Supervised PAC Anomaly Detection. CoRR (2022)
Yinshuang Xu, Jiahui Lei, Edgar Dobriban, Kostas Daniilidis
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces. ICML (2022)
Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani
PAC Prediction Sets for Meta-Learning. CoRR (2022)
Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban, Kostas Daniilidis
Learning Augmentation Distributions using Transformed Risk Minimization. CoRR (2021)
Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney
Sparse sketches with small inversion bias. COLT (2021)
Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani
PAC Prediction Sets Under Covariate Shift. CoRR (2021)
Yaodong Yu, Zitong Yang, Edgar Dobriban, Jacob Steinhardt, Yi Ma
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition. CoRR (2021)
Licong Lin, Edgar Dobriban
What Causes the Test Error? Going Beyond Bias-Variance via ANOVA. J. Mach. Learn. Res. 22 (2021)
Dominic Richards, Edgar Dobriban, Patrick Rebeschini
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models? CoRR (2021)
Fan Yang, Sifan Liu, Edgar Dobriban, David P. Woodruff
How to Reduce Dimension With PCA and Random Projections? IEEE Trans. Inf. Theory 67 (12) (2021)
Lingjiao Chen, Leshang Chen, Hongyi Wang, Susan B. Davidson, Edgar Dobriban
Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients. CoRR (2021)
Edgar Dobriban, Hamed Hassani, David Hong, Alexander Robey
Provable tradeoffs in adversarially robust classification. CoRR (2020)
Yinjun Wu, Edgar Dobriban, Susan B. Davidson
DeltaGrad: Rapid retraining of machine learning models. CoRR (2020)
Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel Ward, Qiang Liu
Implicit Regularization and Convergence for Weight Normalization. NeurIPS (2020)
Sifan Liu, Edgar Dobriban
Ridge Regression: Structure, Cross-Validation, and Sketching. ICLR (2020)
Yue Sheng, Edgar Dobriban
One-shot Distributed Ridge Regression in High Dimensions. ICML (2020)
Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani
The Implicit Regularization of Stochastic Gradient Flow for Least Squares. CoRR (2020)
Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani
The Implicit Regularization of Stochastic Gradient Flow for Least Squares. ICML (2020)
Jonathan Lacotte, Sifan Liu, Edgar Dobriban, Mert Pilanci
Limiting Spectrum of Randomized Hadamard Transform and Optimal Iterative Sketching Methods. CoRR (2020)
Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney
Sparse sketches with small inversion bias. CoRR (2020)
Edgar Dobriban, Yue Sheng
WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions. J. Mach. Learn. Res. 21 (2020)
Yinjun Wu, Edgar Dobriban, Susan B. Davidson
DeltaGrad: Rapid retraining of machine learning models. ICML (2020)
Jonathan Lacotte, Sifan Liu, Edgar Dobriban, Mert Pilanci
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform. NeurIPS (2020)
Shuxiao Chen, Edgar Dobriban, Jane H. Lee
A Group-Theoretic Framework for Data Augmentation. NeurIPS (2020)
Licong Lin, Edgar Dobriban
What causes the test error? Going beyond bias-variance via ANOVA. CoRR (2020)
Edgar Dobriban, Yue Sheng
One-shot distributed ridge regression in high dimensions. CoRR (2019)
Edgar Dobriban, Sifan Liu
Asymptotics for Sketching in Least Squares Regression. NeurIPS (2019)
Shuxiao Chen, Edgar Dobriban, Jane H. Lee
Invariance reduces Variance: Understanding Data Augmentation in Deep Learning and Beyond. CoRR (2019)
Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel Ward, Qiang Liu
Implicit Regularization of Normalization Methods. CoRR (2019)
Edgar Dobriban, Sifan Liu
A New Theory for Sketching in Linear Regression. CoRR (2018)
Afonso S. Bandeira, Edgar Dobriban, Dustin G. Mixon, William F. Sawin
Certifying the Restricted Isometry Property is Hard. IEEE Trans. Inf. Theory 59 (6) (2013)
Afonso S. Bandeira, Edgar Dobriban, Dustin G. Mixon, William F. Sawin
Certifying the restricted isometry property is hard CoRR (2012)