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Aditi Raghunathan
Aditi Raghunathan
PhD Student, Stanford University
stanford.edu의 이메일 확인됨 - 홈페이지
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Certified defenses against adversarial examples
A Raghunathan, J Steinhardt, P Liang
arXiv preprint arXiv:1801.09344, 2018
7612018
Unlabeled data improves adversarial robustness
Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang
Advances in Neural Information Processing Systems 32, 2019
3942019
Semidefinite relaxations for certifying robustness to adversarial examples
A Raghunathan, J Steinhardt, PS Liang
Advances in Neural Information Processing Systems 31, 2018
3162018
On the opportunities and risks of foundation models
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2021
2982021
Certified robustness to adversarial word substitutions
R Jia, A Raghunathan, K Göksel, P Liang
arXiv preprint arXiv:1909.00986, 2019
1642019
Adversarial training can hurt generalization
A Raghunathan, SM Xie, F Yang, JC Duchi, P Liang
arXiv preprint arXiv:1906.06032, 2019
1362019
An investigation of why overparameterization exacerbates spurious correlations
S Sagawa, A Raghunathan, PW Koh, P Liang
International Conference on Machine Learning, 8346-8356, 2020
1242020
Understanding and mitigating the tradeoff between robustness and accuracy
A Raghunathan, SM Xie, F Yang, J Duchi, P Liang
arXiv preprint arXiv:2002.10716, 2020
1062020
The pitfalls of simplicity bias in neural networks
H Shah, K Tamuly, A Raghunathan, P Jain, P Netrapalli
Advances in Neural Information Processing Systems 33, 9573-9585, 2020
952020
Just train twice: Improving group robustness without training group information
EZ Liu, B Haghgoo, AS Chen, A Raghunathan, PW Koh, S Sagawa, ...
International Conference on Machine Learning, 6781-6792, 2021
712021
DROCC: Deep robust one-class classification
S Goyal, A Raghunathan, M Jain, HV Simhadri, P Jain
International Conference on Machine Learning, 3711-3721, 2020
702020
Robust encodings: A framework for combating adversarial typos
E Jones, R Jia, A Raghunathan, P Liang
arXiv preprint arXiv:2005.01229, 2020
622020
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
S Dathathri, K Dvijotham, A Kurakin, A Raghunathan, J Uesato, RR Bunel, ...
Advances in Neural Information Processing Systems 33, 5318-5331, 2020
452020
Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
JP Miller, R Taori, A Raghunathan, S Sagawa, PW Koh, V Shankar, ...
International Conference on Machine Learning, 7721-7735, 2021
402021
Fine-tuning can distort pretrained features and underperform out-of-distribution
A Kumar, A Raghunathan, R Jones, T Ma, P Liang
arXiv preprint arXiv:2202.10054, 2022
252022
Decoupling exploration and exploitation for meta-reinforcement learning without sacrifices
EZ Liu, A Raghunathan, P Liang, C Finn
International conference on machine learning, 6925-6935, 2021
172021
Estimating the unseen from multiple populations
A Raghunathan, G Valiant, J Zou
International Conference on Machine Learning, 2855-2863, 2017
172017
Estimation from indirect supervision with linear moments
A Raghunathan, R Frostig, J Duchi, P Liang
International conference on machine learning, 2568-2577, 2016
112016
An explanation of in-context learning as implicit bayesian inference
SM Xie, A Raghunathan, P Liang, T Ma
arXiv preprint arXiv:2111.02080, 2021
102021
Maximum weighted loss discrepancy
F Khani, A Raghunathan, P Liang
arXiv preprint arXiv:1906.03518, 2019
92019
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