485 citations · 1.5k across the 72 of their papers we have counts for
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Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Maxwell T. West, Sarah M. Erfani, Christopher Leckie +3
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry. Despite their accuracy and sophisticati…
Backdoor Attacks on Time Series: A Generative Approach
Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani +1
Backdoor attacks have emerged as one of the major security threats to deep learning models as they can easily control the model's test-time predictions by pre-injecting a backdoor…
COLLIDER: A Robust Training Framework for Backdoor Data
Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
Deep neural network (DNN) classifiers are vulnerable to backdoor attacks. An adversary poisons some of the training data in such attacks by installing a trigger. The goal is to mak…
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity
Andrew C. Cullen, Paul Montague, Shijie Liu +2
In response to subtle adversarial examples flipping classifications of neural network models, recent research has promoted certified robustness as a solution. There, invariance of…
Adversarial Coreset Selection for Efficient Robust Training
Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
Neural networks are vulnerable to adversarial attacks: adding well-crafted, imperceptible perturbations to their input can modify their output. Adversarial training is one of the m…
Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems
Yixin Su, Yunxiang Zhao, Sarah Erfani +2
Detecting beneficial feature interactions is essential in recommender systems, and existing approaches achieve this by examining all the possible feature interactions. However, the…