Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Recent studies reveal that a well-trained deep reinforcement learning (RL) policy can be
particularly vulnerable to adversarial perturbations on input observations. Therefore, it is …

We investigate the problem of corruption robustness in offline reinforcement learning (RL)
with general function approximation, where an adversary can corrupt each sample in the …

The rapid growth in the number of devices and their connectivity has enlarged the attack
surface and made cyber systems more vulnerable. As attackers become increasingly …

A trustworthy reinforcement learning algorithm should be competent in solving challenging
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …

We propose a model-based reinforcement learning framework to derive untargeted
poisoning attacks against federated learning (FL) systems. Our framework first approximates …

Evaluating the worst-case performance of a reinforcement learning (RL) agent under the
strongest/optimal adversarial perturbations on state observations (within some constraints) …

Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …