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Journal Articles
- 2024
[j3]Xi Jiang
, Shinan Liu, Aaron Gember-Jacobson, Arjun Nitin Bhagoji, Paul Schmitt, Francesco Bronzino, Nick Feamster:
NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation. Proc. ACM Meas. Anal. Comput. Syst. 8(1): 11:1-11:32 (2024)- 2023
- [j2]Shinan Liu, Francesco Bronzino, Paul Schmitt, Arjun Nitin Bhagoji, Nick Feamster, Hector Garcia Crespo, Timothy Coyle, Brian Ward:
LEAF: Navigating Concept Drift in Cellular Networks. PACMNET 1(CoNEXT2): 7:1-7:24 (2023) - 2021
- [j1]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 14(1-2): 1-210 (2021)
Conference and Workshop Papers
- 2024
- [c22]Brennan Schaffner, Arjun Nitin Bhagoji, Siyuan Cheng, Jacqueline Mei, Jay L. Shen, Grace Wang, Marshini Chetty, Nick Feamster, Genevieve Lakier, Chenhao Tan:
"Community Guidelines Make this the Best Party on the Internet": An In-Depth Study of Online Platforms' Content Moderation Policies. CHI 2024: 486:1-486:16 - [c21]Andrew Chu, Xi Jiang, Shinan Liu, Arjun Nitin Bhagoji, Francesco Bronzino, Paul Schmitt, Nick Feamster:
Feasibility of State Space Models for Network Traffic Generation. NAIC 2024: 9-17 - [c20]Wenxin Ding, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng:
Towards Scalable and Robust Model Versioning. SaTML 2024: 592-611 - [c19]Xi Jiang, Shinan Liu, Aaron Gember-Jacobson, Arjun Nitin Bhagoji, Paul Schmitt, Francesco Bronzino, Nick Feamster:
NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation. SIGMETRICS/Performance (Abstracts) 2024: 85-86 - 2023
- [c18]Jacob Alexander Markson Brown, Xi Jiang, Van Hong Tran, Arjun Nitin Bhagoji, Nguyen Phong Hoang, Nick Feamster, Prateek Mittal, Vinod Yegneswaran:
Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning. KDD 2023: 3750-3761 - [c17]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 - 2022
- [c16]Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal:
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. AISTATS 2022: 7587-7624 - [c15]Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Ben Y. Zhao, Heather Zheng, Prateek Mittal:
Understanding Robust Learning through the Lens of Representation Similarities. NeurIPS 2022 - [c14]Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Heather Zheng, Ben Y. Zhao:
Finding Naturally Occurring Physical Backdoors in Image Datasets. NeurIPS 2022 - [c13]Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao:
Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks. USENIX Security Symposium 2022: 3575-3592 - 2021
- [c12]Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao:
Patch-based Defenses against Web Fingerprinting Attacks. AISec@CCS 2021: 97-109 - [c11]Emily Wenger, Josephine Passananti, Arjun Nitin Bhagoji, Yuanshun Yao, Haitao Zheng, Ben Y. Zhao:
Backdoor Attacks Against Deep Learning Systems in the Physical World. CVPR 2021: 6206-6215 - [c10]Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal:
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries. ICML 2021: 863-873 - [c9]Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag, Prateek Mittal:
PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking. USENIX Security Symposium 2021: 2237-2254 - 2019
- [c8]Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal:
Analyzing the Robustness of Open-World Machine Learning. AISec@CCS 2019: 105-116 - [c7]Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin B. Calo:
Analyzing Federated Learning through an Adversarial Lens. ICML 2019: 634-643 - [c6]Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal:
Lower Bounds on Adversarial Robustness from Optimal Transport. NeurIPS 2019: 7496-7508 - 2018
- [c5]Vikash Sehwag, Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal:
Not All Pixels are Born Equal: An Analysis of Evasion Attacks under Locality Constraints. CCS 2018: 2285-2287 - [c4]Arjun Nitin Bhagoji, Daniel Cullina, Chawin Sitawarin, Prateek Mittal:
Enhancing robustness of machine learning systems via data transformations. CISS 2018: 1-5 - [c3]Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song:
Practical Black-Box Attacks on Deep Neural Networks Using Efficient Query Mechanisms. ECCV (12) 2018: 158-174 - [c2]Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song:
Black-box Attacks on Deep Neural Networks via Gradient Estimation. ICLR (Workshop) 2018 - [c1]Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal:
PAC-learning in the presence of adversaries. NeurIPS 2018: 228-239
Informal and Other Publications
- 2024
- [i24]Wenxin Ding, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng:
Towards Scalable and Robust Model Versioning. CoRR abs/2401.09574 (2024) - [i23]Brennan Schaffner, Arjun Nitin Bhagoji, Siyuan Cheng, Jacqueline Mei, Jay L. Shen, Grace Wang, Marshini Chetty, Nick Feamster, Genevieve Lakier, Chenhao Tan:
"Community Guidelines Make this the Best Party on the Internet": An In-Depth Study of Online Platforms' Content Moderation Policies. CoRR abs/2405.05225 (2024) - [i22]Andrew Chu, Xi Jiang, Shinan Liu, Arjun Nitin Bhagoji, Francesco Bronzino, Paul Schmitt, Nick Feamster:
Feasibility of State Space Models for Network Traffic Generation. CoRR abs/2406.02784 (2024) - 2023
- [i21]Jacob Alexander Markson Brown, Xi Jiang, Van Hong Tran, Arjun Nitin Bhagoji, Nguyen Phong Hoang, Nick Feamster, Prateek Mittal, Vinod Yegneswaran:
Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning. CoRR abs/2302.02031 (2023) - [i20]Sihui Dai, Wenxin Ding, Arjun Nitin Bhagoji, Daniel Cullina, Ben Y. Zhao, Haitao Zheng, Prateek Mittal:
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker. CoRR abs/2302.10722 (2023) - [i19]Xi Jiang, Shinan Liu, Aaron Gember-Jacobson, Arjun Nitin Bhagoji, Paul Schmitt, Francesco Bronzino, Nick Feamster:
NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation. CoRR abs/2310.08543 (2023) - 2022
- [i18]Huiying Li, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng:
Can Backdoor Attacks Survive Time-Varying Models? CoRR abs/2206.04677 (2022) - [i17]Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Ben Y. Zhao, Prateek Mittal:
Understanding Robust Learning through the Lens of Representation Similarities. CoRR abs/2206.09868 (2022) - [i16]Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Haitao Zheng, Ben Y. Zhao:
Natural Backdoor Datasets. CoRR abs/2206.10673 (2022) - 2021
- [i15]Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao:
A Real-time Defense against Website Fingerprinting Attacks. CoRR abs/2102.04291 (2021) - [i14]Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal:
Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries. CoRR abs/2104.08382 (2021) - [i13]Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao:
Traceback of Data Poisoning Attacks in Neural Networks. CoRR abs/2110.06904 (2021) - [i12]Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal:
SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. CoRR abs/2112.06274 (2021) - 2020
- [i11]Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag, Prateek Mittal:
PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields. CoRR abs/2005.10884 (2020) - [i10]Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji, Prateek Mittal:
A Critical Evaluation of Open-World Machine Learning. CoRR abs/2007.04391 (2020) - 2019
- [i9]Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal:
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples. CoRR abs/1905.01726 (2019) - [i8]Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal:
Lower Bounds on Adversarial Robustness from Optimal Transport. CoRR abs/1909.12272 (2019) - [i7]Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, Sen Zhao:
Advances and Open Problems in Federated Learning. CoRR abs/1912.04977 (2019) - 2018
- [i6]Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Prateek Mittal, Mung Chiang:
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos. CoRR abs/1801.02780 (2018) - [i5]Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal:
DARTS: Deceiving Autonomous Cars with Toxic Signs. CoRR abs/1802.06430 (2018) - [i4]Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal:
PAC-learning in the presence of evasion adversaries. CoRR abs/1806.01471 (2018) - [i3]Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin B. Calo:
Analyzing Federated Learning through an Adversarial Lens. CoRR abs/1811.12470 (2018) - 2017
- [i2]Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal:
Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers. CoRR abs/1704.02654 (2017) - [i1]Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song:
Exploring the Space of Black-box Attacks on Deep Neural Networks. CoRR abs/1712.09491 (2017)
Coauthor Index
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last updated on 2024-10-07 22:11 CEST by the dblp team
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