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Sihui Dai
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2020 – today
- 2024
- [c8]Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarathkrishna Swaminathan, Sihui Dai, Aurélie C. Lozano, Georgios Kollias, Vijil Chenthamarakshan, Jirí Navrátil, Soham Dan, Pin-Yu Chen:
Larimar: Large Language Models with Episodic Memory Control. ICML 2024 - [c7]Chong Xiang, Tong Wu, Sihui Dai, Jonathan Petit, Suman Jana, Prateek Mittal:
PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses. USENIX Security Symposium 2024 - [i10]Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarath Swaminathan, Sihui Dai, Aurélie C. Lozano, Georgios Kollias, Vijil Chenthamarakshan, Jirí Navrátil, Soham Dan, Pin-Yu Chen:
Larimar: Large Language Models with Episodic Memory Control. CoRR abs/2403.11901 (2024) - [i9]Sihui Dai, Chong Xiang
, Tong Wu, Prateek Mittal:
Position Paper: Beyond Robustness Against Single Attack Types. CoRR abs/2405.01349 (2024) - 2023
- [c6]Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal:
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks. ICML 2023: 6760-6785 - [c5]Sihui Dai, Wenxin Ding, Arjun Nitin Bhagoji, Daniel Cullina, Heather Zheng, Ben Zhao, Prateek Mittal:
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker. NeurIPS 2023 - [i8]Sihui Dai, Wenxin Ding, Arjun Nitin Bhagoji
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker. CoRR abs/2302.10722 (2023) - [i7]Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal:
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks. CoRR abs/2302.10980 (2023) - [i6]Chong Xiang
PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses. CoRR abs/2310.13076 (2023) - 2022
- [c4]Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal:
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? ICLR 2022 - [c3]Sihui Dai, Saeed Mahloujifar, Prateek Mittal:
Formulating Robustness Against Unforeseen Attacks. NeurIPS 2022 - [c2]Sihui Dai, Saeed Mahloujifar, Prateek Mittal:
Parameterizing Activation Functions for Adversarial Robustness. SP (Workshops) 2022: 80-87 - [i5]Sihui Dai, Saeed Mahloujifar, Prateek Mittal:
Formulating Robustness Against Unforeseen Attacks. CoRR abs/2204.13779 (2022) - 2021
- [i4]Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal:
Improving Adversarial Robustness Using Proxy Distributions. CoRR abs/2104.09425 (2021) - [i3]Sihui Dai, Saeed Mahloujifar, Prateek Mittal:
Parameterizing Activation Functions for Adversarial Robustness. CoRR abs/2110.05626 (2021) - 2020
- [c1]Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar:
Neural Networks with Recurrent Generative Feedback. NeurIPS 2020 - [i2]Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar:
Neural Networks with Recurrent Generative Feedback. CoRR abs/2007.09200 (2020)
2010 – 2019
- 2019
- [i1]Yujia Huang, Sihui Dai, Tan M. Nguyen, Richard G. Baraniuk, Anima Anandkumar:
Out-of-Distribution Detection Using Neural Rendering Generative Models. CoRR abs/1907.04572 (2019)
Coauthor Index
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