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ORCIDEdgar Dobriban
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2020 – today
- 2024
[c23]Xinmeng Huang, Shuo Li, Mengxin Yu, Matteo Sesia, Hamed Hassani, Insup Lee, Osbert Bastani, Edgar Dobriban:
Uncertainty in Language Models: Assessment through Rank-Calibration. EMNLP 2024: 284-312- [c22]Wenwen Si, Sangdon Park, Insup Lee, Edgar Dobriban, Osbert Bastani:
PAC Prediction Sets Under Label Shift. ICLR 2024 - [c21]Behrad Moniri, Donghwan Lee, Hamed Hassani, Edgar Dobriban:
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks. ICML 2024 - [i43]Xianli Zeng, Guang Cheng, Edgar Dobriban:
Bayes-Optimal Fair Classification with Linear Disparity Constraints via Pre-, In-, and Post-processing. CoRR abs/2402.02817 (2024) - [i42]Xianli Zeng, Guang Cheng, Edgar Dobriban:
Minimax Optimal Fair Classification with Bounded Demographic Disparity. CoRR abs/2403.18216 (2024) - [i41]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 abs/2404.01318 (2024) - [i40]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 abs/2404.03163 (2024) - [i39]Xinmeng Huang, Shuo Li, Edgar Dobriban, Osbert Bastani, Hamed Hassani, Dongsheng Ding:
One-Shot Safety Alignment for Large Language Models via Optimal Dualization. CoRR abs/2405.19544 (2024) - [i38]Patrick Chao, Edgar Dobriban, Hamed Hassani:
Watermarking Language Models with Error Correcting Codes. CoRR abs/2406.10281 (2024) - [i37]Behrad Moniri, Hamed Hassani, Edgar Dobriban:
Evaluating the Performance of Large Language Models via Debates. CoRR abs/2406.11044 (2024) - [i36]Yan Sun, Pratik Chaudhari, Ian J. Barnett, Edgar Dobriban:
A Confidence Interval for the ℓ2 Expected Calibration Error. CoRR abs/2408.08998 (2024) - 2023
- [j9]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): 4135-4171 (2023) - [j8]Donghwan Lee, Xinmeng Huang, Hamed Hassani, Edgar Dobriban:
T-Cal: An Optimal Test for the Calibration of Predictive Models. J. Mach. Learn. Res. 24: 335:1-335:72 (2023) - [j7]Matteo Sesia, Stefano Favaro, Edgar Dobriban:
Conformal Frequency Estimation using Discrete Sketched Data with Coverage for Distinct Queries. J. Mach. Learn. Res. 24: 348:1-348:80 (2023) - [j6]Edgar Dobriban, Hamed Hassani, David Hong, Alexander Robey:
Provable Tradeoffs in Adversarially Robust Classification. IEEE Trans. Inf. Theory 69(12): 7793-7822 (2023) - [j5]Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Kostas Daniilidis, Edgar Dobriban:
Learning Augmentation Distributions using Transformed Risk Minimization. Trans. Mach. Learn. Res. 2023 (2023) - [c20]Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban, Kostas Daniilidis:
SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space. ICLR 2023 - [c19]Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani:
Demystifying Disagreement-on-the-Line in High Dimensions. ICML 2023: 19053-19093 - [c18]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: 4603-4607 - [i35]Donghwan Lee, Behrad Moniri, Xinmeng Huang, Edgar Dobriban, Hamed Hassani:
Demystifying Disagreement-on-the-Line in High Dimensions. CoRR abs/2301.13371 (2023) - [i34]Xinmeng Huang, Kan Xu, Donghwan Lee, Hamed Hassani, Hamsa Bastani, Edgar Dobriban:
Optimal Heterogeneous Collaborative Linear Regression and Contextual Bandits. CoRR abs/2306.06291 (2023) - [i33]Patrick Chao, Edgar Dobriban:
Statistical Estimation Under Distribution Shift: Wasserstein Perturbations and Minimax Theory. CoRR abs/2308.01853 (2023) - [i32]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 abs/2310.07891 (2023) - [i31]Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong:
Jailbreaking Black Box Large Language Models in Twenty Queries. CoRR abs/2310.08419 (2023) - [i30]Wenwen Si, Sangdon Park, Insup Lee, Edgar Dobriban, Osbert Bastani:
PAC Prediction Sets Under Label Shift. CoRR abs/2310.12964 (2023) - [i29]Edgar Dobriban, Mengxin Yu:
SymmPI: Predictive Inference for Data with Group Symmetries. CoRR abs/2312.16160 (2023) - 2022
- [c17]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: 7104-7114 - [c16]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: 1499-1509 - [c15]Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani:
PAC Prediction Sets Under Covariate Shift. ICLR 2022 - [c14]Yinshuang Xu, Jiahui Lei, Edgar Dobriban, Kostas Daniilidis:
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces. ICML 2022: 24596-24614 - [c13]Shuo Li, Xiayan Ji, Edgar Dobriban, Oleg Sokolsky, Insup Lee:
PAC-Wrap: Semi-Supervised PAC Anomaly Detection. KDD 2022: 945-955 - [c12]Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani:
PAC Prediction Sets for Meta-Learning. NeurIPS 2022 - [c11]Xinmeng Huang, Donghwan Lee, Edgar Dobriban, Hamed Hassani:
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints. NeurIPS 2022 - [c10]Xianli Zeng, Edgar Dobriban, Guang Cheng:
Fair Bayes-Optimal Classifiers Under Predictive Parity. NeurIPS 2022 - [i28]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 abs/2201.02331 (2022) - [i27]Xianli Zeng, Edgar Dobriban, Guang Cheng:
Bayes-Optimal Classifiers under Group Fairness. CoRR abs/2202.09724 (2022) - [i26]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 abs/2202.12967 (2022) - [i25]Donghwan Lee, Xinmeng Huang, Hamed Hassani, Edgar Dobriban:
T-Cal: An optimal test for the calibration of predictive models. CoRR abs/2203.01850 (2022) - [i24]Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban, Kostas Daniilidis:
SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space. CoRR abs/2204.02394 (2022) - [i23]Xianli Zeng, Edgar Dobriban, Guang Cheng:
Fair Bayes-Optimal Classifiers Under Predictive Parity. CoRR abs/2205.07182 (2022) - [i22]Shuo Li, Xiayan Ji, Edgar Dobriban, Oleg Sokolsky, Insup Lee:
PAC-Wrap: Semi-Supervised PAC Anomaly Detection. CoRR abs/2205.10798 (2022) - [i21]Xinmeng Huang, Donghwan Lee, Edgar Dobriban, Hamed Hassani:
Collaborative Learning of Distributions under Heterogeneity and Communication Constraints. CoRR abs/2206.00707 (2022) - [i20]Souradeep Dutta, Yahan Yang, Elena Bernardis, Edgar Dobriban, Insup Lee:
Memory Classifiers: Two-stage Classification for Robustness in Machine Learning. CoRR abs/2206.05323 (2022) - [i19]Yinshuang Xu, Jiahui Lei, Edgar Dobriban, Kostas Daniilidis:
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces. CoRR abs/2206.08362 (2022) - [i18]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 abs/2206.09120 (2022) - [i17]Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani:
PAC Prediction Sets for Meta-Learning. CoRR abs/2207.02440 (2022) - 2021
- [j4]Licong Lin, Edgar Dobriban:
What Causes the Test Error? Going Beyond Bias-Variance via ANOVA. J. Mach. Learn. Res. 22: 155:1-155:82 (2021) - [j3]Fan Yang, Sifan Liu, Edgar Dobriban, David P. Woodruff:
How to Reduce Dimension With PCA and Random Projections? IEEE Trans. Inf. Theory 67(12): 8154-8189 (2021) - [c9]Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney:
Sparse sketches with small inversion bias. COLT 2021: 1467-1510 - [i16]Yaodong Yu, Zitong Yang, Edgar Dobriban, Jacob Steinhardt, Yi Ma:
Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition. CoRR abs/2103.09947 (2021) - [i15]Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani:
PAC Prediction Sets Under Covariate Shift. CoRR abs/2106.09848 (2021) - [i14]Dominic Richards, Edgar Dobriban, Patrick Rebeschini:
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models? CoRR abs/2108.11872 (2021) - [i13]Lingjiao Chen, Leshang Chen, Hongyi Wang, Susan B. Davidson, Edgar Dobriban:
Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients. CoRR abs/2110.01595 (2021) - [i12]Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban, Kostas Daniilidis:
Learning Augmentation Distributions using Transformed Risk Minimization. CoRR abs/2111.08190 (2021) - 2020
- [j2]Edgar Dobriban, Yue Sheng:
WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions. J. Mach. Learn. Res. 21: 66:1-66:52 (2020) - [c8]Sifan Liu, Edgar Dobriban:
Ridge Regression: Structure, Cross-Validation, and Sketching. ICLR 2020 - [c7]Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani:
The Implicit Regularization of Stochastic Gradient Flow for Least Squares. ICML 2020: 233-244 - [c6]Yue Sheng, Edgar Dobriban:
One-shot Distributed Ridge Regression in High Dimensions. ICML 2020: 8763-8772 - [c5]Yinjun Wu, Edgar Dobriban, Susan B. Davidson:
DeltaGrad: Rapid retraining of machine learning models. ICML 2020: 10355-10366 - [c4]Shuxiao Chen, Edgar Dobriban, Jane H. Lee:
A Group-Theoretic Framework for Data Augmentation. NeurIPS 2020 - [c3]Jonathan Lacotte, Sifan Liu, Edgar Dobriban, Mert Pilanci:
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform. NeurIPS 2020 - [c2]Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel A. Ward, Qiang Liu:
Implicit Regularization and Convergence for Weight Normalization. NeurIPS 2020 - [i11]Jonathan Lacotte, Sifan Liu, Edgar Dobriban, Mert Pilanci:
Limiting Spectrum of Randomized Hadamard Transform and Optimal Iterative Sketching Methods. CoRR abs/2002.00864 (2020) - [i10]Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani:
The Implicit Regularization of Stochastic Gradient Flow for Least Squares. CoRR abs/2003.07802 (2020) - [i9]Edgar Dobriban, Hamed Hassani, David Hong, Alexander Robey:
Provable tradeoffs in adversarially robust classification. CoRR abs/2006.05161 (2020) - [i8]Yinjun Wu, Edgar Dobriban, Susan B. Davidson:
DeltaGrad: Rapid retraining of machine learning models. CoRR abs/2006.14755 (2020) - [i7]Licong Lin, Edgar Dobriban:
What causes the test error? Going beyond bias-variance via ANOVA. CoRR abs/2010.05170 (2020) - [i6]Michal Derezinski, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney:
Sparse sketches with small inversion bias. CoRR abs/2011.10695 (2020)
2010 – 2019
- 2019
- [c1]Edgar Dobriban, Sifan Liu:
Asymptotics for Sketching in Least Squares Regression. NeurIPS 2019: 3670-3680 - [i5]Edgar Dobriban, Yue Sheng:
One-shot distributed ridge regression in high dimensions. CoRR abs/1903.09321 (2019) - [i4]Shuxiao Chen, Edgar Dobriban, Jane H. Lee:
Invariance reduces Variance: Understanding Data Augmentation in Deep Learning and Beyond. CoRR abs/1907.10905 (2019) - [i3]Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel A. Ward, Qiang Liu:
Implicit Regularization of Normalization Methods. CoRR abs/1911.07956 (2019) - 2018
- [i2]Edgar Dobriban, Sifan Liu:
A New Theory for Sketching in Linear Regression. CoRR abs/1810.06089 (2018) - 2013
- [j1]Afonso S. Bandeira, Edgar Dobriban, Dustin G. Mixon, William F. Sawin:
Certifying the Restricted Isometry Property is Hard. IEEE Trans. Inf. Theory 59(6): 3448-3450 (2013) - 2012
- [i1]Afonso S. Bandeira, Edgar Dobriban, Dustin G. Mixon, William F. Sawin:
Certifying the restricted isometry property is hard. CoRR abs/1204.1580 (2012)
Coauthor Index
aka: Hamed Hassani
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