Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging
and trusting statistical and deep learning models, as well as interpreting their predictions …

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 …

Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to
its ability to achieve high performance in a range of environments with little manual …

Deep Reinforcement Learning (DRL) is an essential subfield of Artificial Intelligence (AI),
where agents interact with environments to learn policies for solving complex tasks. In recent …

Conversational recommender systems (CRS) explicitly solicit users' preferences for
improved recommendations on the fly. Most existing CRS solutions count on a single policy …

Deep learning models are widely used in daily life, which bring great convenience to our
lives, but they are vulnerable to attacks. How to build an attack system with strong …

Artificial intelligence (AI) systems are becoming critical components of today's IT landscapes.
Their resilience against attacks and other environmental influences needs to be ensured just …

The efficient and theoretically sound uncertainty quantification is crucial for building trust in
deep learning models. This has spurred a growing interest in conformal prediction (CP), a …

Recently, we have witnessed the success of deep reinforcement learning (DRL) in many
security applications, ranging from malware mutation to selfish blockchain mining. Like all …

Rising usage of deep neural networks to perform decision making in critical applications like
medical diagnosis and fi-nancial analysis have raised concerns regarding their reliability …