Certifying the Restricted Isometry Property is Hard. - Afonso S. Bandeira; Edgar Dobriban; Dustin G. Mixon; William F. Sawin (IEEE Trans. Inf. Theory, 2013)

A New Theory for Sketching in Linear Regression. - Edgar Dobriban; Sifan Liu (CoRR, 2018)

Asymptotics for Sketching in Least Squares Regression. - Edgar Dobriban; Sifan Liu (NeurIPS, 2019)

Invariance reduces Variance: Understanding Data Augmentation in Deep Learning and Beyond. - Shuxiao Chen; Edgar Dobriban; Jane H. Lee (CoRR, 2019)

Implicit Regularization of Normalization Methods. - Xiaoxia Wu; Edgar Dobriban; Tongzheng Ren; Shanshan Wu; Zhiyuan Li; Suriya Gunasekar; Rachel A. Ward; Qiang Liu 001 (CoRR, 2019)

Ridge Regression: Structure, Cross-Validation, and Sketching. - Sifan Liu; Edgar Dobriban (ICLR, 2020)

The Implicit Regularization of Stochastic Gradient Flow for Least Squares. - Alnur Ali; Edgar Dobriban; Ryan J. Tibshirani (ICML, 2020)

One-shot Distributed Ridge Regression in High Dimensions. - Yue Sheng; Edgar Dobriban (ICML, 2020)

DeltaGrad: Rapid retraining of machine learning models. - Yinjun Wu; Edgar Dobriban; Susan B. Davidson (ICML, 2020)

A Group-Theoretic Framework for Data Augmentation. - Shuxiao Chen; Edgar Dobriban; Jane H. Lee (NeurIPS, 2020)

Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform. - Jonathan Lacotte; Sifan Liu; Edgar Dobriban; Mert Pilanci (NeurIPS, 2020)

Implicit Regularization and Convergence for Weight Normalization. - Xiaoxia Wu; Edgar Dobriban; Tongzheng Ren; Shanshan Wu; Zhiyuan Li; Suriya Gunasekar; Rachel A. Ward; Qiang Liu 001 (NeurIPS, 2020)

Limiting Spectrum of Randomized Hadamard Transform and Optimal Iterative Sketching Methods. - Jonathan Lacotte; Sifan Liu; Edgar Dobriban; Mert Pilanci (CoRR, 2020)

WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions. - Edgar Dobriban; Yue Sheng (J. Mach. Learn. Res., 2020)

Sparse sketches with small inversion bias. - Michal Derezinski; Zhenyu Liao 001; Edgar Dobriban; Michael W. Mahoney (COLT, 2021)

Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition. - Yaodong Yu; Zitong Yang; Edgar Dobriban; Jacob Steinhardt; Yi Ma 001 (CoRR, 2021)

Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models? - Dominic Richards; Edgar Dobriban; Patrick Rebeschini (CoRR, 2021)

Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients. - Lingjiao Chen; Leshang Chen; Hongyi Wang 001; Susan B. Davidson; Edgar Dobriban (CoRR, 2021)

What Causes the Test Error? Going Beyond Bias-Variance via ANOVA. - Licong Lin; Edgar Dobriban (J. Mach. Learn. Res., 2021)

How to Reduce Dimension With PCA and Random Projections? - Fan Yang; Sifan Liu; Edgar Dobriban; David P. Woodruff (IEEE Trans. Inf. Theory, 2021)

iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection. - Ramneet Kaur; Susmit Jha; Anirban Roy; Sangdon Park 001; Edgar Dobriban; Oleg Sokolsky; Insup Lee 001 (AAAI, 2022)

Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates. - Souradeep Dutta; Kaustubh Sridhar; Osbert Bastani; Edgar Dobriban; James Weimer; Insup Lee 001; Julia Parish-Morris (CoRL, 2022)

PAC Prediction Sets Under Covariate Shift. - Sangdon Park 001; Edgar Dobriban; Insup Lee 001; Osbert Bastani (ICLR, 2022)

Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces. - Yinshuang Xu; Jiahui Lei; Edgar Dobriban; Kostas Daniilidis (ICML, 2022)

PAC-Wrap: Semi-Supervised PAC Anomaly Detection. - Shuo Li; Xiayan Ji; Edgar Dobriban; Oleg Sokolsky; Insup Lee 001 (KDD, 2022)

PAC Prediction Sets for Meta-Learning. - Sangdon Park 001; Edgar Dobriban; Insup Lee 001; Osbert Bastani (NeurIPS, 2022)

Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints. - Xinmeng Huang; Donghwan Lee; Edgar Dobriban; Hamed Hassani (NeurIPS, 2022)

Fair Bayes-Optimal Classifiers Under Predictive Parity. - Xianli Zeng; Edgar Dobriban; Guang Cheng (NeurIPS, 2022)

Bayes-Optimal Classifiers under Group Fairness. - Xianli Zeng; Edgar Dobriban; Guang Cheng (CoRR, 2022)

Collaborative Learning of Distributions under Heterogeneity and Communication Constraints. - Xinmeng Huang; Donghwan Lee; Edgar Dobriban; Hamed Hassani (CoRR, 2022)

Memory Classifiers: Two-stage Classification for Robustness in Machine Learning. - Souradeep Dutta; Yahan Yang; Elena Bernardis; Edgar Dobriban; Insup Lee 001 (CoRR, 2022)

SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space. - Evangelos Chatzipantazis; Stefanos Pertigkiozoglou; Edgar Dobriban; Kostas Daniilidis (ICLR, 2023)

Demystifying Disagreement-on-the-Line in High Dimensions. - Donghwan Lee; Behrad Moniri; Xinmeng Huang; Edgar Dobriban; Hamed Hassani (ICML, 2023)

Automatically Predicting Perceived Conversation Quality in a Pediatric Sample Enriched for Autism. - Yahan Yang; Sunghye Cho; Maxine Covello; Azia Knox; Osbert Bastani; James Weimer; Edgar Dobriban; Robert T. Schultz; Insup Lee 001; Julia Parish-Morris (INTERSPEECH, 2023)

Optimal Heterogeneous Collaborative Linear Regression and Contextual Bandits. - Xinmeng Huang; Kan Xu; Donghwan Lee; Hamed Hassani; Hamsa Bastani; Edgar Dobriban (CoRR, 2023)

Statistical Estimation Under Distribution Shift: Wasserstein Perturbations and Minimax Theory. - Patrick Chao; Edgar Dobriban (CoRR, 2023)

SymmPI: Predictive Inference for Data with Group Symmetries. - Edgar Dobriban; Mengxin Yu (CoRR, 2023)

Pursuit of a discriminative representation for multiple subspaces via sequential games. - Druv Pai; Michael Psenka; Chih-Yuan Chiu; Manxi Wu; Edgar Dobriban; Yi Ma 001 (J. Frankl. Inst., 2023)

T-Cal: An Optimal Test for the Calibration of Predictive Models. - Donghwan Lee; Xinmeng Huang; Hamed Hassani; Edgar Dobriban (J. Mach. Learn. Res., 2023)

Conformal Frequency Estimation using Discrete Sketched Data with Coverage for Distinct Queries. - Matteo Sesia; Stefano Favaro; Edgar Dobriban (J. Mach. Learn. Res., 2023)

Learning Augmentation Distributions using Transformed Risk Minimization. - Evangelos Chatzipantazis; Stefanos Pertigkiozoglou; Kostas Daniilidis; Edgar Dobriban (Trans. Mach. Learn. Res., 2023)

Provable Tradeoffs in Adversarially Robust Classification. - Edgar Dobriban; Hamed Hassani; David Hong; Alexander Robey (IEEE Trans. Inf. Theory, 2023)

Uncertainty in Language Models: Assessment through Rank-Calibration. - Xinmeng Huang; Shuo Li; Mengxin Yu; Matteo Sesia; Hamed Hassani; Insup Lee 001; Osbert Bastani; Edgar Dobriban (EMNLP, 2024)

PAC Prediction Sets Under Label Shift. - Wenwen Si; Sangdon Park 001; Insup Lee 001; Edgar Dobriban; Osbert Bastani (ICLR, 2024)

A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks. - Behrad Moniri; Donghwan Lee; Hamed Hassani; Edgar Dobriban (ICML, 2024)

JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models. - 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 001 (NeurIPS, 2024)

One-Shot Safety Alignment for Large Language Models via Optimal Dualization. - Xinmeng Huang; Shuo Li; Edgar Dobriban; Osbert Bastani; Hamed Hassani; Dongsheng Ding (NeurIPS, 2024)

Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing. - Xianli Zeng; Guang Cheng; Edgar Dobriban (CoRR, 2024)

Minimax Optimal Fair Classification with Bounded Demographic Disparity. - Xianli Zeng; Guang Cheng; Edgar Dobriban (CoRR, 2024)

Watermarking Language Models with Error Correcting Codes. - Patrick Chao; Edgar Dobriban; Hamed Hassani (CoRR, 2024)

A Confidence Interval for the ℓ2 Expected Calibration Error. - Yan Sun; Pratik Chaudhari; Ian J. Barnett; Edgar Dobriban (CoRR, 2024)

Evaluating the Performance of Large Language Models via Debates. - Behrad Moniri; Hamed Hassani; Edgar Dobriban (NAACL (Findings), 2025)

Jailbreaking Black Box Large Language Models in Twenty Queries. - Patrick Chao; Alexander Robey; Edgar Dobriban; Hamed Hassani; George J. Pappas; Eric Wong 001 (SaTML, 2025)

Likelihood-Ratio Regularized Quantile Regression: Adapting Conformal Prediction to High-Dimensional Covariate Shifts. - Sunay Joshi; Shayan Kiyani; George J. Pappas; Edgar Dobriban; Hamed Hassani (CoRR, 2025)

Synthetic-Powered Predictive Inference. - Meshi Bashari; Roy Maor Lotan; Yonghoon Lee; Edgar Dobriban; Yaniv Romano (CoRR, 2025)

Foundations of Top-k Decoding For Language Models. - Georgy Noarov; Soham Mallick; Tao Wang; Sunay Joshi; Yan Sun; Yangxinyu Xie; Mengxin Yu; Edgar Dobriban (CoRR, 2025)

Conformal Information Pursuit for Interactively Guiding Large Language Models. - Kwan Ho Ryan Chan; Yuyan Ge; Edgar Dobriban; Hamed Hassani; René Vidal (CoRR, 2025)