Recent sharpness-aware minimisation (SAM) is known to find flat minima which is beneficial
for better generalisation with improved robustness. SAM essentially modifies the loss …

The right to be forgotten calls for efficient machine unlearning techniques that make trained
machine learning models forget a cohort of data. The combination of training and unlearning …

Adversarial training (AT) has become the de-facto standard to obtain models robust against
adversarial examples. However, AT exhibits severe robust overfitting: cross-entropy loss on …

Although machine learning is widely used in practice, little is known about practitioners'
understanding of potential security challenges. In this work, we close this substantial gap …

Studying the sensitivity of weight perturbation in neural networks and its impacts on model
performance, including generalization and robustness, is an active research topic due to its …

Applying a pre-trained large model to downstream tasks is prohibitive under resource-
constrained conditions. Recent dominant approaches for addressing efficiency issues …

Adversarial Robustness for Machine Learning summarizes the recent progress on this topic
and introduces popular algorithms on adversarial attack, defense and veri? cation. Sections …

Adversarial robustness studies the worst-case performance of a machine learning model to
ensure safety and reliability. With the proliferation of deep-learning-based technology, the …

Deep neural network (DNN) accelerators received considerable attention in past years due
to saved energy compared to mainstream hardware. Low-voltage operation of DNN …

We argue that the vulnerability of model parameters is of crucial value to the study of model
robustness and generalization but little research has been devoted to understanding this …