Reinforcement learning (RL) is one of the active fields in machine learning, demonstrating
remarkable potential in tackling real-world challenges. Despite its promising prospects, this …

In the field of eXplainable Artificial Intelligence (XAI), the generation of interpretable models
that are able to match the performance of state-of-the-art deep learning methods is one of …

Tabular data remains one of the most prevalent data types across a wide range of real-world
applications, yet effective representation learning for this domain poses unique challenges …

Interactive and interpretable robot learning can help to democratize robots, placing the
power of assistive robotic systems in the hands of endusers. While machine learning-based …

Interpretability in machine learning is critical for the safe deployment of learned policies
across legally-regulated and safety-critical domains. While gradient-based approaches in …

Objective Artificial intelligence (AI)‐based decision support systems (DSS) are utilized in
medicine but underlying decision‐making processes are usually unknown. Explainable AI …

Safefy-critical domains often employ autonomous agents which follow a sequential decision-
making setup, whereby the agent follows a policy to dictate the appropriate action at each …

The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing
development of highly autonomous vehicles, largely due to advances in deep learning, the …

In the field of eXplainable Artificial Intelligence (XAI), the generation of interpretable models
that are able to match the performance of state-of-the-art deep learning methods is one of …

There is an increasing interest in learning reward functions that model human intent and
human preferences. However, many frameworks use blackbox learning methods that, while …