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UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach


Jul 11, 2021
Su Wang, Seyyedali Hosseinalipour, Maria Gorlatova, Christopher G. Brinton, Mung Chiang


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Improving Adversarial Robustness Using Proxy Distributions


Apr 19, 2021
Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal

* 24 pages, 5 figures, 4 tables 

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SSD: A Unified Framework for Self-Supervised Outlier Detection


Mar 22, 2021
Vikash Sehwag, Mung Chiang, Prateek Mittal

* ICLR 2021 

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Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation


Jan 04, 2021
Su Wang, Mengyuan Lee, Seyyedali Hosseinalipour, Roberto Morabito, Mung Chiang, Christopher G. Brinton

* This paper is accepted for publication in the proceedings of 2021 IEEE International Conference on Computer Communications (INFOCOM) 

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RobustBench: a standardized adversarial robustness benchmark


Oct 19, 2020
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein


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Fast-Convergent Federated Learning


Jul 26, 2020
Hung T. Nguyen, Vikash Sehwag, Seyyedali Hosseinalipour, Christopher G. Brinton, Mung Chiang, H. Vincent Poor


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Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks


Jun 24, 2020
Vikash Sehwag, Rajvardhan Oak, Mung Chiang, Prateek Mittal

* 6 pages, 5 figures 

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From Federated Learning to Fog Learning: Towards Large-Scale Distributed Machine Learning in Heterogeneous Wireless Networks


Jun 07, 2020
Seyyedali Hosseinalipour, Christopher G. Brinton, Vaneet Aggarwal, Huaiyu Dai, Mung Chiang

* 7 pages, 4 figures 

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AppStreamer: Reducing Storage Requirements of Mobile Games through Predictive Streaming


Dec 16, 2019
Nawanol Theera-Ampornpunt, Shikhar Suryavansh, Sameer Manchanda, Rajesh Panta, Kaustubh Joshi, Mostafa Ammar, Mung Chiang, Saurabh Bagchi

* 12 pages; EWSN 2020 

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Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples


May 05, 2019
Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal

* 18 pages, 5 figures, 9 tables 

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An Estimation and Analysis Framework for the Rasch Model


Jun 09, 2018
Andrew S. Lan, Mung Chiang, Christoph Studer

* To be presented at ICML 2018 

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DARTS: Deceiving Autonomous Cars with Toxic Signs


May 31, 2018
Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal

* Submitted to ACM CCS 2018; Extended version of [1801.02780] Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos 

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Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos


Mar 26, 2018
Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Prateek Mittal, Mung Chiang

* Extended abstract accepted for the 1st Deep Learning and Security Workshop; 5 pages, 4 figures 

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Linearized Binary Regression


Feb 01, 2018
Andrew S. Lan, Mung Chiang, Christoph Studer

* To be presented at CISS (http://ee-ciss.princeton.edu/

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Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization


Jun 27, 2014
Felix Ming Fai Wong, Zhenming Liu, Mung Chiang


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