14 citations · 21 across the 2 of their papers we have counts for
10 papers
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML
Sijia Liu, Songtao Lu, Xiangyi Chen +5
In this paper, we study the problem of constrained robust (min-max) optimization ina black-box setting, where the desired optimizer cannot access the gradients of the objective fun…
There are No Bit Parts for Sign Bits in Black-Box Attacks
Abdullah Al-Dujaili, Una-May O'Reilly
We present a black-box adversarial attack algorithm which sets new state-of-the-art model evasion rates for query efficiency in the and metrics, where only l…
Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection
Jwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili +2
Timely prediction of clinically critical events in Intensive Care Unit (ICU) is important for improving care and survival rate. Most of the existing approaches are based on the app…
Lipizzaner: A System That Scales Robust Generative Adversarial Network Training
Tom Schmiedlechner, Ignavier Ng Zhi Yong, Abdullah Al-Dujaili +2
GANs are difficult to train due to convergence pathologies such as mode and discriminator collapse. We introduce Lipizzaner, an open source software system that allows machine lear…
AST-Based Deep Learning for Detecting Malicious PowerShell
Gili Rusak, Abdullah Al-Dujaili, Una-May O'Reilly
With the celebrated success of deep learning, some attempts to develop effective methods for detecting malicious PowerShell programs employ neural nets in a traditional natural lan…
Towards Distributed Coevolutionary GANs
Abdullah Al-Dujaili, Tom Schmiedlechner, and Erik Hemberg +1
Generative Adversarial Networks (GANs) have become one of the dominant methods for deep generative modeling. Despite their demonstrated success on multiple vision tasks, GANs are d…