Empirically Measuring Concentration: Fundamental Limits on Intrinsic
Robustness

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Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness

29 May 2019

Saeed Mahloujifar

Mohammad Mahmoody

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Papers citing "Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness"

11 / 11 papers shown

Title
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness Ambar Pal Huaijin Hao Rene Vidal 100 8 0 28 Sep 2023
Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions Ezgi Korkmaz Jonah Brown-Cohen AAML 65 9 0 09 Jun 2023
Investigating Vulnerabilities of Deep Neural Policies Ezgi Korkmaz AAML 55 34 0 30 Aug 2021
HASI: Hardware-Accelerated Stochastic Inference, A Defense Against Adversarial Machine Learning Attacks Mohammad Hossein Samavatian Saikat Majumdar Kristin Barber R. Teodorescu AAML 118 4 0 09 Jun 2021
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? Vikash Sehwag Saeed Mahloujifar Tinashe Handina Sihui Dai Chong Xiang M. Chiang Prateek Mittal OOD 102 131 0 19 Apr 2021
$Improved Estimation of Concentration Under $\ell_p$-Norm Distance Metrics Using Half Spaces$ Improved Estimation of Concentration Under $\ell_p$ -Norm Distance Metrics Using Half Spaces Jack Prescott Xiao Zhang David Evans 49 5 0 24 Mar 2021
Provable tradeoffs in adversarially robust classification Yan Sun Hamed Hassani David Hong Alexander Robey 107 56 0 09 Jun 2020
One Neuron to Fool Them All Anshuman Suri David Evans AAML 31 4 0 20 Mar 2020
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models Xiao Zhang Jinghui Chen Quanquan Gu David Evans 76 17 0 01 Mar 2020
Local intrinsic dimensionality estimators based on concentration of measure Jonathan Bac A. Zinovyev 42 10 0 31 Jan 2020
Lower Bounds for Adversarially Robust PAC Learning Dimitrios I. Diochnos Saeed Mahloujifar Mohammad Mahmoody AAML 80 26 0 13 Jun 2019