| Robust learning meets generative models: Can proxy distributions improve adversarial robustness? V Sehwag, S Mahloujifar, T Handina, S Dai, C Xiang, M Chiang, P Mittal arXiv preprint arXiv:2104.09425, 2021 | 144 | 2021 |
| Neural Networks with Recurrent Generative Feedback Y Huang, J Gornet, S Dai, Z Yu, T Nguyen, DY Tsao, A Anandkumar arXiv preprint arXiv:2007.09200, 2020 | 48 | 2020 |
| Improving adversarial robustness using proxy distributions V Sehwag, S Mahloujifar, T Handina, S Dai, C Xiang, M Chiang, P Mittal arXiv preprint arXiv:2104.09425, 2021 | 35 | 2021 |
| Parameterizing Activation Functions for Adversarial Robustness S Dai, S Mahloujifar, P Mittal arXiv preprint arXiv:2110.05626, 2021 | 32 | 2021 |
| Formulating Robustness Against Unforeseen Attacks S Dai, S Mahloujifar, P Mittal arXiv preprint arXiv:2204.13779, 2022 | 9 | 2022 |
| Larimar: Large Language Models with Episodic Memory Control P Das, S Chaudhury, E Nelson, I Melnyk, S Swaminathan, S Dai, ... arXiv preprint arXiv:2403.11901, 2024 | 8 | 2024 |
| Parameterizing activation functions for adversarial robustness. In 2022 IEEE Security and Privacy Workshops (SPW) S Dai, S Mahloujifar, P Mittal IEEE 2 (6), 8, 2022 | 8 | 2022 |
| Multi-task bayesian optimization via gaussian process upper confidence bound S Dai, J Song, Y Yue ICML 2020 Workshop on Real World Experiment Design and Active Learning, 2020 | 8 | 2020 |
| Brain-inspired Robust Vision using Convolutional Neural Networks with Feedback Y Huang, S Dai, T Nguyen, P Bao, D Tsao, RG Baraniuk, A Anandkumar | 8 | 2019 |
| Out-of-Distribution Detection Using Neural Rendering Generative Models Y Huang, S Dai, T Nguyen, RG Baraniuk, A Anandkumar arXiv preprint arXiv:1907.04572, 2019 | 8 | 2019 |
| MultiRobustBench: Benchmarking Robustness Against Multiple Attacks S Dai, S Mahloujifar, C Xiang, V Sehwag, PY Chen, P Mittal arXiv preprint arXiv:2302.10980, 2023 | 6 | 2023 |
| Patchcure: Improving certifiable robustness, model utility, and computation efficiency of adversarial patch defenses C Xiang, T Wu, S Dai, J Petit, S Jana, P Mittal arXiv preprint arXiv:2310.13076, 2023 | 2 | 2023 |
| Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker S Dai, W Ding, AN Bhagoji, D Cullina, BY Zhao, H Zheng, P Mittal arXiv preprint arXiv:2302.10722, 2023 | 1 | 2023 |
| ROBUSTNESS FROM PERCEPTION S Mahloujifar, C Xiang, V Sehwag, S Dai, P Mittal | 1* | |
| Position Paper: Beyond Robustness Against Single Attack Types S Dai, C Xiang, T Wu, P Mittal arXiv preprint arXiv:2405.01349, 2024 | | 2024 |
| Lower Bounds on 0-1 Loss for Multi-class Classification with a Test-time Attacker S Dai, W Ding, AN Bhagoji, D Cullina, P Mittal, BY Zhao NeurIPS ML Safety Workshop, 2022 | | 2022 |
| Learner Knowledge Levels in Adversarial Machine Learning S Dai, P Mittal | | |
| Neural Networks with Recurrent Generative Feedback YHJGS Dai, ZYT Nguyen, DYTA Anandkumar | | |
