33 citations · 92 across the 16 of their papers we have counts for
23 papers
Local Intrinsic Dimensionality Signals Adversarial Perturbations
Sandamal Weerasinghe, Tansu Alpcan, Sarah M. Erfani +2
The vulnerability of machine learning models to adversarial perturbations has motivated a significant amount of research under the broad umbrella of adversarial machine learning. S…
No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees
R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein +1
Automating physical database design has remained a long-term interest in database research due to substantial performance gains afforded by optimised structures. Despite significan…
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation
Jun Wang, Chang Xu, Francisco Guzman +3
Mistranslated numbers have the potential to cause serious effects, such as financial loss or medical misinformation. In this work we develop comprehensive assessments of the robust…
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning
Jun Wang, Chang Xu, Francisco Guzman +4
Neural machine translation systems are known to be vulnerable to adversarial test inputs, however, as we show in this paper, these systems are also vulnerable to training attacks.…
TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness
Zhuolin Yang, Linyi Li, Xiaojun Xu +6
Adversarial Transferability is an intriguing property - adversarial perturbation crafted against one model is also effective against another model, while these models are from diff…
Not fit for Purpose: A critical analysis of the 'Five Safes'
Chris Culnane, Benjamin I. P. Rubinstein, David Watts
Adopted by government agencies in Australia, New Zealand and the UK as policy instrument or as embodied into legislation, the 'Five Safes' framework aims to manage risks of releasi…