125 citations · 237 across the 11 of their papers we have counts for
17 papers
Adversarial Plannning
Valentin Vie, Ryan Sheatsley, Sophia Beyda +4
Planning algorithms are used in computational systems to direct autonomous behavior. In a canonical application, for example, planning for autonomous vehicles is used to automate t…
Joint Coreset Construction and Quantization for Distributed Machine Learning
Hanlin Lu, Changchang Liu, Shiqiang Wang +4
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication…
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
Todd Huster, Jeremy E. J. Cohen, Zinan Lin +5
Generative adversarial networks (GANs) are often billed as "universal distribution learners", but precisely what distributions they can represent and learn is still an open questio…
You Do (Not) Belong Here: Detecting DPI Evasion Attacks with Context Learning
Shitong Zhu, Shasha Li, Zhongjie Wang +5
As Deep Packet Inspection (DPI) middleboxes become increasingly popular, a spectrum of adversarial attacks have emerged with the goal of evading such middleboxes. Many of these att…
Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency
Shasha Li, Shitong Zhu, Sudipta Paul +5
There has been a recent surge in research on adversarial perturbations that defeat Deep Neural Networks (DNNs) in machine vision; most of these perturbation-based attacks target ob…
Sharing Models or Coresets: A Study based on Membership Inference Attack
Hanlin Lu, Changchang Liu, Ting He +2
Distributed machine learning generally aims at training a global model based on distributed data without collecting all the data to a centralized location, where two different appr…