Arjun Nitin Bhagoji

56 papers A* 14A 1Misc 1Journal 31Unranked 8

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Adapting to Evolving Adversaries with Regularized Continual Robust Training. ICML Sihui Dai, Christian Cianfarani, Vikash Sehwag, Prateek Mittal, Arjun Nitin Bhagoji
2025	J	`jnl`	Adapting to Evolving Adversaries with Regularized Continual Robust Training. CoRR Sihui Dai, Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Prateek Mittal
2025	J	`jnl`	NetSSM: Multi-Flow and State-Aware Network Trace Generation using State-Space Models. CoRR Andrew Chu, Xi Jiang, Shinan Liu, Arjun Nitin Bhagoji, Francesco Bronzino, Paul Schmitt, Nick Feamster
2025	—	`conf`	Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitch. ACL (1) Prarabdh Shukla, Wei Yin Chong, Yash Patel, Brennan Schaffner, Danish Pruthi, Arjun Nitin Bhagoji
2025	J	`jnl`	Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitch. CoRR Prarabdh Shukla, Wei Yin Chong, Yash Patel, Brennan Schaffner, Danish Pruthi, Arjun Nitin Bhagoji
2024	A*	`conf`	"Community Guidelines Make this the Best Party on the Internet": An In-Depth Study of Online Platforms' Content Moderation Policies. CHI Brennan Schaffner, Arjun Nitin Bhagoji, Siyuan Cheng, Jacqueline Mei, Jay L. Shen, Grace Wang, Marshini Chetty, Nick Feamster, Genevieve Lakier, Chenhao Tan
2024	J	`jnl`	"Community Guidelines Make this the Best Party on the Internet": An In-Depth Study of Online Platforms' Content Moderation Policies. CoRR Brennan Schaffner, Arjun Nitin Bhagoji, Siyuan Cheng, Jacqueline Mei, Jay L. Shen, Grace Wang, Marshini Chetty, Nick Feamster, Genevieve Lakier, Chenhao Tan
2024	—	`conf`	Feasibility of State Space Models for Network Traffic Generation. NAIC Andrew Chu, Xi Jiang, Shinan Liu, Arjun Nitin Bhagoji, Francesco Bronzino, Paul Schmitt, Nick Feamster
2024	J	`jnl`	Feasibility of State Space Models for Network Traffic Generation. CoRR Andrew Chu, Xi Jiang, Shinan Liu, Arjun Nitin Bhagoji, Francesco Bronzino, Paul Schmitt, Nick Feamster
2024	J	`jnl`	MYCROFT: Towards Effective and Efficient External Data Augmentation. CoRR Zain Sarwar, Van Tran, Arjun Nitin Bhagoji, Nick Feamster, Ben Y. Zhao, Supriyo Chakraborty
2024	—	`conf`	NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation. SIGMETRICS/Performance (Abstracts) Xi Jiang, Shinan Liu, Aaron Gember-Jacobson, Arjun Nitin Bhagoji, Paul Schmitt, Francesco Bronzino, Nick Feamster
2024	J	`jnl`	NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation. Proc. ACM Meas. Anal. Comput. Syst. Xi Jiang, Shinan Liu, Aaron Gember-Jacobson, Arjun Nitin Bhagoji, Paul Schmitt, Francesco Bronzino, Nick Feamster
2024	—	`conf`	Towards Scalable and Robust Model Versioning. SaTML Wenxin Ding, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng
2024	J	`jnl`	Towards Scalable and Robust Model Versioning. CoRR Wenxin Ding, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng
2023	A*	`conf`	Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning. KDD Jacob Alexander Markson Brown, Xi Jiang, Van Hong Tran, Arjun Nitin Bhagoji, Nguyen Phong Hoang, Nick Feamster, Prateek Mittal, Vinod Yegneswaran
2023	J	`jnl`	Augmenting Rule-based DNS Censorship Detection at Scale with Machine Learning. CoRR Jacob Alexander Markson Brown, Xi Jiang, Van Hong Tran, Arjun Nitin Bhagoji, Nguyen Phong Hoang, Nick Feamster, Prateek Mittal, Vinod Yegneswaran
2023	A*	`conf`	Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker. NeurIPS Sihui Dai, Wenxin Ding, Arjun Nitin Bhagoji, Daniel Cullina, Heather Zheng, Ben Zhao, Prateek Mittal
2023	J	`jnl`	Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker. CoRR Sihui Dai, Wenxin Ding, Arjun Nitin Bhagoji, Daniel Cullina, Ben Y. Zhao, Haitao Zheng, Prateek Mittal
2023	J	`jnl`	LEAF: Navigating Concept Drift in Cellular Networks. PACMNET Shinan Liu, Francesco Bronzino, Paul Schmitt, Arjun Nitin Bhagoji, Nick Feamster, Hector Garcia Crespo, Timothy Coyle, Brian Ward
2023	J	`jnl`	NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation. CoRR Xi Jiang, Shinan Liu, Aaron Gember-Jacobson, Arjun Nitin Bhagoji, Paul Schmitt, Francesco Bronzino, Nick Feamster
2022	J	`jnl`	Can Backdoor Attacks Survive Time-Varying Models? CoRR Huiying Li, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng
2022	A*	`conf`	Finding Naturally Occurring Physical Backdoors in Image Datasets. NeurIPS Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Heather Zheng, Ben Y. Zhao
2022	J	`jnl`	Natural Backdoor Datasets. CoRR Emily Wenger, Roma Bhattacharjee, Arjun Nitin Bhagoji, Josephine Passananti, Emilio Andere, Haitao Zheng, Ben Y. Zhao
2022	A*	`conf`	Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks. USENIX Security Symposium Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao
2022	A	`conf`	SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. AISTATS Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal
2022	A*	`conf`	Understanding Robust Learning through the Lens of Representation Similarities. NeurIPS Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Ben Y. Zhao, Heather Zheng, Prateek Mittal
2022	J	`jnl`	Understanding Robust Learning through the Lens of Representation Similarities. CoRR Christian Cianfarani, Arjun Nitin Bhagoji, Vikash Sehwag, Ben Y. Zhao, Prateek Mittal
2021	J	`jnl`	A Real-time Defense against Website Fingerprinting Attacks. CoRR Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao
2021	J	`jnl`	Advances and Open Problems in Federated Learning. Found. Trends Mach. Learn. 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
2021	A*	`conf`	Backdoor Attacks Against Deep Learning Systems in the Physical World. CVPR Emily Wenger, Josephine Passananti, Arjun Nitin Bhagoji, Yuanshun Yao, Haitao Zheng, Ben Y. Zhao
2021	A*	`conf`	Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries. ICML Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal
2021	J	`jnl`	Lower Bounds on Cross-Entropy Loss in the Presence of Test-time Adversaries. CoRR Arjun Nitin Bhagoji, Daniel Cullina, Vikash Sehwag, Prateek Mittal
2021	—	`conf`	Patch-based Defenses against Web Fingerprinting Attacks. AISec@CCS Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao
2021	A*	`conf`	PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking. USENIX Security Symposium Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag, Prateek Mittal
2021	J	`jnl`	SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. CoRR Ashwinee Panda, Saeed Mahloujifar, Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal
2021	J	`jnl`	Traceback of Data Poisoning Attacks in Neural Networks. CoRR Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao
2020	J	`jnl`	A Critical Evaluation of Open-World Machine Learning. CoRR Liwei Song, Vikash Sehwag, Arjun Nitin Bhagoji, Prateek Mittal
2020	J	`jnl`	PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields. CoRR Chong Xiang, Arjun Nitin Bhagoji, Vikash Sehwag, Prateek Mittal
2020	—	`—`	The Role of Data Geometry in Adversarial Machine Learning. Arjun Nitin Bhagoji
2019	J	`jnl`	Advances and Open Problems in Federated Learning. CoRR 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
2019	A*	`conf`	Analyzing Federated Learning through an Adversarial Lens. ICML Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin B. Calo
2019	—	`conf`	Analyzing the Robustness of Open-World Machine Learning. AISec@CCS Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal
2019	J	`jnl`	Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples. CoRR Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal
2019	A*	`conf`	Lower Bounds on Adversarial Robustness from Optimal Transport. NeurIPS Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal
2019	J	`jnl`	Lower Bounds on Adversarial Robustness from Optimal Transport. CoRR Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal
2018	J	`jnl`	Analyzing Federated Learning through an Adversarial Lens. CoRR Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin B. Calo
2018	—	`conf`	Black-box Attacks on Deep Neural Networks via Gradient Estimation. ICLR (Workshop) Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song
2018	J	`jnl`	DARTS: Deceiving Autonomous Cars with Toxic Signs. CoRR Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal
2018	Misc	`conf`	Enhancing robustness of machine learning systems via data transformations. CISS Arjun Nitin Bhagoji, Daniel Cullina, Chawin Sitawarin, Prateek Mittal
2018	A*	`conf`	Not All Pixels are Born Equal: An Analysis of Evasion Attacks under Locality Constraints. CCS Vikash Sehwag, Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal
2018	A*	`conf`	PAC-learning in the presence of adversaries. NeurIPS Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal
2018	J	`jnl`	PAC-learning in the presence of evasion adversaries. CoRR Daniel Cullina, Arjun Nitin Bhagoji, Prateek Mittal
2018	—	`conf`	Practical Black-Box Attacks on Deep Neural Networks Using Efficient Query Mechanisms. ECCV (12) Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song
2018	J	`jnl`	Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos. CoRR Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Prateek Mittal, Mung Chiang
2017	J	`jnl`	Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers. CoRR Arjun Nitin Bhagoji, Daniel Cullina, Prateek Mittal
2017	J	`jnl`	Exploring the Space of Black-box Attacks on Deep Neural Networks. CoRR Arjun Nitin Bhagoji, Warren He, Bo Li, Dawn Song