Sarah Erfani

Anomaly detection in non-stationary data: Ensemble based self-adaptive OCSVM

2016

With the emergence of data streaming applications that produce large data in motion, anomaly detection in non-stationary environments has become a major research focus. Unknown and unstable behaviour of data over time, limits the application …

Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

2018 · Own your potential (DEAKIN)

© Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved. Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to …

Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems

2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Detecting beneficial feature interactions is essential in recommender systems, and existing approaches achieve this by examining all the possible feature interactions. However, the cost of examining all the possible higher-order feature interactions is prohibitive (exponentially …

Backdoor Attacks on Time Series: A Generative Approach

2022 · arXiv (Cornell University)

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 trigger into the model at training time. …

Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning

2025 · arXiv (Cornell University)

The current state-of-the-art backdoor attacks against Reinforcement Learning (RL) rely upon unrealistically permissive access models, that assume the attacker can read (or even write) the victim's policy parameters, observations, or rewards. In this work, we …