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ShadowNet: A Secure and Efficient System for On-device Model Inference

Nov 11, 2020
Zhichuang Sun, Ruimin Sun, Long Lu, Somesh Jha

* single column, 21 pages (30 pages include appendix), 7 figures 

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An Attack on InstaHide: Is Private Learning Possible with Instance Encoding?

Nov 10, 2020
Nicholas Carlini, Samuel Deng, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Shuang Song, Abhradeep Thakurta, Florian Tramer


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Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers

Jul 29, 2020
Jayaram Raghuram, Varun Chandrasekaran, Somesh Jha, Suman Banerjee

* 32 pages, 13 figures 

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Abstract Universal Approximation for Neural Networks

Jul 14, 2020
Zi Wang, Aws Albarghouthi, Somesh Jha


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Robust Learning against Logical Adversaries

Jul 01, 2020
Yizhen Wang, Xiaozhu Meng, Mihai Christodorescu, Somesh Jha


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Robust Out-of-distribution Detection via Informative Outlier Mining

Jun 26, 2020
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha


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Continuous Release of Data Streams under both Centralized and Local Differential Privacy

May 24, 2020
Tianhao Wang, Joann Qiongna Chen, Zhikun Zhang, Dong Su, Yueqiang Cheng, Zhou Li, Ninghui Li, Somesh Jha


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Representation Bayesian Risk Decompositions and Multi-Source Domain Adaptation

Apr 22, 2020
Xi Wu, Yang Guo, Jiefeng Chen, Yingyu Liang, Somesh Jha, Prasad Chalasani

* 25 pages, 6 figures 

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Robust Out-of-distribution Detection for Neural Networks

Apr 05, 2020
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha


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Obliviousness Makes Poisoning Adversaries Weaker

Mar 26, 2020
Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Abhradeep Thakurta


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Robust Out-of-distribution Detection in Neural Networks

Mar 24, 2020
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha


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Analyzing Accuracy Loss in Randomized Smoothing Defenses

Mar 03, 2020
Yue Gao, Harrison Rosenberg, Kassem Fawaz, Somesh Jha, Justin Hsu

* 19 pages, 6 figures, 2 tables 

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CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods

Feb 18, 2020
Wei Zhang, Thomas Kobber Panum, Somesh Jha, Prasad Chalasani, David Page


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Query-Efficient Physical Hard-Label Attacks on Deep Learning Visual Classification

Feb 17, 2020
Ryan Feng, Jiefeng Chen, Nelson Manohar, Earlence Fernandes, Somesh Jha, Atul Prakash


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Semantic Robustness of Models of Source Code

Feb 07, 2020
Goutham Ramakrishnan, Jordan Henkel, Zi Wang, Aws Albarghouthi, Somesh Jha, Thomas Reps

* 19 pages 

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On Need for Topology-Aware Generative Models for Manifold-Based Defenses

Oct 08, 2019
Uyeong Jang, Susmit Jha, Somesh Jha


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Generating Semantic Adversarial Examples with Differentiable Rendering

Oct 02, 2019
Lakshya Jain, Wilson Wu, Steven Chen, Uyeong Jang, Varun Chandrasekaran, Sanjit Seshia, Somesh Jha


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On Need for Topology Awareness of Generative Models

Sep 11, 2019
Uyeong Jang, Susmit Jha, Somesh Jha


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Practical and Robust Privacy Amplification with Multi-Party Differential Privacy

Aug 30, 2019
Tianhao Wang, Min Xu, Bolin Ding, Jingren Zhou, Ninghui Li, Somesh Jha


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Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models

May 30, 2019
Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha


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Adversarially Robust Learning Could Leverage Computational Hardness

May 28, 2019
Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody


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Enhancing ML Robustness Using Physical-World Constraints

May 26, 2019
Varun Chandrasekaran, Brian Tang, Varsha Pendyala, Kassem Fawaz, Somesh Jha, Xi Wu


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Robust Attribution Regularization

May 23, 2019
Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha


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Attribution-driven Causal Analysis for Detection of Adversarial Examples

Mar 14, 2019
Susmit Jha, Sunny Raj, Steven Lawrence Fernandes, Sumit Kumar Jha, Somesh Jha, Gunjan Verma, Brian Jalaian, Ananthram Swami

* 11 pages, 6 figures 

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Model Extraction and Active Learning

Dec 04, 2018
Varun Chandrasekaran, Kamalika Chaudhuri, Irene Giacomelli, Somesh Jha, Songbai Yan


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Privacy-Preserving Collaborative Prediction using Random Forests

Nov 21, 2018
Irene Giacomelli, Somesh Jha, Ross Kleiman, David Page, Kyonghwan Yoon

* Accepted at the AMIA Informatics Summit 2019 

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Adversarial Learning and Explainability in Structured Datasets

Oct 26, 2018
Prasad Chalasani, Somesh Jha, Aravind Sadagopan, Xi Wu

* 33 pages, 20 figures, 2 tables 

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