Aditi Raghunathan
Aditi Raghunathan
PhD Student, Stanford University
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
Certified defenses against adversarial examples
A Raghunathan, J Steinhardt, P Liang
arXiv preprint arXiv:1801.09344, 2018
6232018
Unlabeled data improves adversarial robustness
Y Carmon, A Raghunathan, L Schmidt, P Liang, JC Duchi
arXiv preprint arXiv:1905.13736, 2019
2402019
Semidefinite relaxations for certifying robustness to adversarial examples
A Raghunathan, J Steinhardt, P Liang
arXiv preprint arXiv:1811.01057, 2018
2272018
Certified robustness to adversarial word substitutions
R Jia, A Raghunathan, K Göksel, P Liang
arXiv preprint arXiv:1909.00986, 2019
1042019
Adversarial training can hurt generalization
A Raghunathan, SM Xie, F Yang, JC Duchi, P Liang
arXiv preprint arXiv:1906.06032, 2019
892019
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
592020
An investigation of why overparameterization exacerbates spurious correlations
S Sagawa, A Raghunathan, PW Koh, P Liang
International Conference on Machine Learning, 8346-8356, 2020
472020
The pitfalls of simplicity bias in neural networks
H Shah, K Tamuly, A Raghunathan, P Jain, P Netrapalli
arXiv preprint arXiv:2006.07710, 2020
362020
Robust encodings: A framework for combating adversarial typos
E Jones, R Jia, A Raghunathan, P Liang
arXiv preprint arXiv:2005.01229, 2020
312020
DROCC: Deep robust one-class classification
S Goyal, A Raghunathan, M Jain, HV Simhadri, P Jain
International Conference on Machine Learning, 3711-3721, 2020
272020
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
S Dathathri, K Dvijotham, A Kurakin, A Raghunathan, J Uesato, R Bunel, ...
arXiv preprint arXiv:2010.11645, 2020
182020
Estimating the unseen from multiple populations
A Raghunathan, G Valiant, J Zou
International Conference on Machine Learning, 2855-2863, 2017
152017
Learning mixture of gaussians with streaming data
A Raghunathan, R Krishnaswamy, P Jain
arXiv preprint arXiv:1707.02391, 2017
82017
Estimation from indirect supervision with linear moments
A Raghunathan, R Frostig, J Duchi, P Liang
International conference on machine learning, 2568-2577, 2016
82016
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
62021
Maximum weighted loss discrepancy
F Khani, A Raghunathan, P Liang
arXiv preprint arXiv:1906.03518, 2019
62019
A reinforcement learning approach to online learning of decision trees
A Garlapati, A Raghunathan, V Nagarajan, B Ravindran
arXiv preprint arXiv:1507.06923, 2015
62015
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
52021
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
52021
Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning
EZ Liu, A Raghunathan, P Liang, C Finn
32020
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Articles 1–20