Dobriban, Edgar

38 publications

NeurIPS 2025 Conformal Inference Under High-Dimensional Covariate Shifts via Likelihood-Ratio Regularization Sunay Joshi, Shayan Kiyani, George J. Pappas, Edgar Dobriban, Hamed Hassani

NeurIPS 2025 Conformal Information Pursuit for Interactively Guiding Large Language Models Kwan Ho Ryan Chan, Yuyan Ge, Edgar Dobriban, Hamed Hassani, Rene Vidal

NeurIPS 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

NeurIPS 2025 Synthetic-Powered Predictive Inference Meshi Bashari, Roy Maor Lotan, Yonghoon Lee, Edgar Dobriban, Yaniv Romano

ICLRW 2025 Watermarking Language Models with Error Correcting Codes Patrick Chao, Yan Sun, Edgar Dobriban, Hamed Hassani

ICML 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

NeurIPS 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

ICMLW 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

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

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

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

ICLR 2024 PAC Prediction Sets Under Label Shift Wenwen Si, Sangdon Park, Insup Lee, Edgar Dobriban, Osbert Bastani

ICLR 2023 $\mathrm{SE}(3)$-Equivariant Attention Networks for Shape Reconstruction in Function Space Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban, Kostas Daniilidis

NeurIPSW 2023 A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks Behrad Moniri, Donghwan Lee, Hamed Hassani, Edgar Dobriban

JMLR 2023 Conformal Frequency Estimation Using Discrete Sketched Data with Coverage for Distinct Queries Matteo Sesia, Stefano Favaro, Edgar Dobriban

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

NeurIPSW 2023 Jailbreaking Black Box Large Language Models in Twenty Queries Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong

TMLR 2023 Learning Augmentation Distributions Using Transformed Risk Minimization Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Kostas Daniilidis, Edgar Dobriban

JMLR 2023 T-Cal: An Optimal Test for the Calibration of Predictive Models Donghwan Lee, Xinmeng Huang, Hamed Hassani, Edgar Dobriban

NeurIPS 2022 Collaborative Learning of Discrete Distributions Under Heterogeneity and Communication Constraints Xinmeng Huang, Donghwan Lee, Edgar Dobriban, Hamed Hassani

CoRL 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, Julia Parish-Morris

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

ICLR 2022 PAC Prediction Sets Under Covariate Shift Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani

NeurIPS 2022 PAC Prediction Sets for Meta-Learning Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani

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

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

COLT 2021 Sparse Sketches with Small Inversion Bias Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael Mahoney

JMLR 2021 What Causes the Test Error? Going Beyond Bias-Variance via ANOVA Licong Lin, Edgar Dobriban

JMLR 2020 A Group-Theoretic Framework for Data Augmentation Shuxiao Chen, Edgar Dobriban, Jane H. Lee

NeurIPS 2020 A Group-Theoretic Framework for Data Augmentation Shuxiao Chen, Edgar Dobriban, Jane Lee

ICML 2020 DeltaGrad: Rapid Retraining of Machine Learning Models Yinjun Wu, Edgar Dobriban, Susan Davidson

NeurIPS 2020 Implicit Regularization and Convergence for Weight Normalization Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel Ward, Qiang Liu

ICML 2020 One-Shot Distributed Ridge Regression in High Dimensions Yue Sheng, Edgar Dobriban

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

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

ICML 2020 The Implicit Regularization of Stochastic Gradient Flow for Least Squares Alnur Ali, Edgar Dobriban, Ryan Tibshirani

JMLR 2020 WONDER: Weighted One-Shot Distributed Ridge Regression in High Dimensions Edgar Dobriban, Yue Sheng

NeurIPS 2019 Asymptotics for Sketching in Least Squares Regression Edgar Dobriban, Sifan Liu