6 citations · 6 across the 3 of their papers we have counts for
3 papers
How robust accuracy suffers from certified training with convex relaxations
Piersilvio De Bartolomeis, Jacob Clarysse, Amartya Sanyal +1
Adversarial attacks pose significant threats to deploying state-of-the-art classifiers in safety-critical applications. Two classes of methods have emerged to address this issue: e…
Margin-based sampling in high dimensions: When being active is less efficient than staying passive
Alexandru Tifrea, Jacob Clarysse, Fanny Yang
It is widely believed that given the same labeling budget, active learning (AL) algorithms like margin-based active learning achieve better predictive performance than passive lear…
Why adversarial training can hurt robust accuracy
Jacob Clarysse, Julia Hörrmann, Fanny Yang
Machine learning classifiers with high test accuracy often perform poorly under adversarial attacks. It is commonly believed that adversarial training alleviates this issue. In thi…