1 citations · 1 across the 2 of their papers we have counts for
3 papers
Poison is Not Traceless: Fully-Agnostic Detection of Poisoning Attacks
Xinglong Chang, Katharina Dost, Gillian Dobbie +1
The performance of machine learning models depends on the quality of the underlying data. Malicious actors can attack the model by poisoning the training data. Current detectors ar…
Fast Adversarial Label-Flipping Attack on Tabular Data
Xinglong Chang, Gillian Dobbie, Jörg Wicker
Machine learning models are increasingly used in fields that require high reliability such as cybersecurity. However, these models remain vulnerable to various attacks, among which…
Memento: Facilitating Effortless, Efficient, and Reliable ML Experiments
Zac Pullar-Strecker, Xinglong Chang, Liam Brydon +3
Running complex sets of machine learning experiments is challenging and time-consuming due to the lack of a unified framework. This leaves researchers forced to spend time implemen…