12 citations · 13 across the 4 of their papers we have counts for
4 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…
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…
Hitting the Target: Stopping Active Learning at the Cost-Based Optimum
Zac Pullar-Strecker, Katharina Dost, Eibe Frank +1
Active learning allows machine learning models to be trained using fewer labels while retaining similar performance to traditional supervised learning. An active learner selects th…
BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability
Xinglong Chang, Katharina Dost, Kaiqi Zhao +4
Adversarial defenses protect machine learning models from adversarial attacks, but are often tailored to one type of model or attack. The lack of information on unknown potential a…