activity
20162022
most citedEvaluating the Robustness of Neural Networks: An Extreme Value Theory Approach

158 citations · 212 across the 8 of their papers we have counts for

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Showing 2020Show all

7 papers · 1 filter

eess.SY2020

Handling Initial Conditions in Vector Fitting for Real Time Modeling of Power System Dynamics

Tommaso Bradde, Samuel Chevalier, Marco De Stefano +2

This paper develops a predictive modeling algorithm, denoted as Real-Time Vector Fitting (RTVF), which is capable of approximating the real-time linearized dynamics of multi-input…

eess.SY2020

Network Topology Invariant Stability Certificates for DC Microgrids with Arbitrary Load Dynamics

Samuel Chevalier, Federico Martin Ibanez, Kathleen Cavanagh +3

DC microgrids are prone to small-signal instabilities due to the presence of tightly regulated loads. This paper develops a decentralized stability certificate which is capable of…

eess.SY2020

Accelerated Probabilistic State Estimation in Distribution Grids via Model Order Reduction

Samuel Chevalier, Luca Schenato, Luca Daniel

This paper applies a custom model order reduction technique to the distribution grid state estimation problem. Specifically, the method targets the situation where, due to pseudo-m…

cs.LG2020

Higher-Order Certification for Randomized Smoothing

Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng +3

Randomized smoothing is a recently proposed defense against adversarial attacks that has achieved SOTA provable robustness against perturbations. A number of publications…

eess.SY2020

Accelerated Probabilistic Power Flow in Electrical Distribution Networks via Model Order Reduction and Neumann Series Expansion

Samuel Chevalier, Luca Schenato, Luca Daniel

This paper develops a computationally efficient algorithm which speeds up the probabilistic power flow (PPF) problem by exploiting the inherently low-rank nature of the voltage pro…

cs.LG2020

Proper Network Interpretability Helps Adversarial Robustness in Classification

Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang +4

Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability (namely, making network interpretation maps visual…