Ethan Rathbun

@uconn.edu

Computer Science and Engineering Department
University of Connecticut



           

https://researchid.co/ethan-rathbun

RESEARCH INTERESTS

Adversarial Machine Learning
Reinforcement Learning
Computational Game Theory

26

Scholar Citations

2

Scholar h-index

1

Scholar i10-index

RECENT SCHOLAR PUBLICATIONS

  • Distilling Adversarial Robustness Using Heterogeneous Teachers
    J Deng, A Palmer, R Mahmood, E Rathbun, J Bi, K Mahmood, D Aguiar
    arXiv preprint arXiv:2402.15586 2024

  • Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning
    E Rathbun, K Mahmood, S Ahmad, C Ding, M Van Dijk
    arXiv preprint arXiv:2211.14669 2022

  • Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
    N Xu, K Mahmood, H Fang, E Rathbun, C Ding, W Wen
    arXiv preprint arXiv:2209.03358 2022

  • Securing the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
    N Xu, K Mahmood, H Fang, E Rathbun, C Ding, W Wen
    arXiv e-prints, arXiv: 2209.03358 2022

  • Back in black: A comparative evaluation of recent state-of-the-art black-box attacks
    K Mahmood, R Mahmood, E Rathbun, M van Dijk
    IEEE Access 10, 998-1019 2021

MOST CITED SCHOLAR PUBLICATIONS

  • Back in black: A comparative evaluation of recent state-of-the-art black-box attacks
    K Mahmood, R Mahmood, E Rathbun, M van Dijk
    IEEE Access 10, 998-1019 2021
    Citations: 16

  • Securing the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
    N Xu, K Mahmood, H Fang, E Rathbun, C Ding, W Wen
    arXiv e-prints, arXiv: 2209.03358 2022
    Citations: 9

  • Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning
    E Rathbun, K Mahmood, S Ahmad, C Ding, M Van Dijk
    arXiv preprint arXiv:2211.14669 2022
    Citations: 1