Multivariate time series (MTS) classification is a challenging and important task in various
domains and real-world applications. Much of prior work on MTS can be roughly divided into …

Reinforcement learning (RL) is broadly employed in human-involved systems to enhance
human outcomes. Off-policy evaluation (OPE) has been pivotal for RL in those realms since …

Reinforcement learning (RL) has been extensively researched for enhancing human-
environment interactions in various human-centric tasks, including e-learning and …

In the realm of reinforcement learning (RL), off-policy evaluation (OPE) holds a pivotal
position, especially in high-stake human-involved scenarios such as e-learning and …

Disease progression modeling (DPM) plays an essential role in characterizing patients'
historical pathways and predicting their future risks. Apprenticeship learning (AL) aims to …

Apprenticeship learning (AL) is a process of inducing effective decision-making policies via
observing and imitating experts' demonstrations. Most existing AL approaches, however, are …

Accurately predicting the clinical endpoint in ICU based on the patient's electronic medical
records (EMRs) is essential for the timely treatment of critically ill patients and allocation of …

The widespread adoption of public and hybrid clouds, along with elastic resources and
various automation tools for dynamic deployment, has accelerated the rapid provisioning of …

Sepsis is a life-threatening organ dysfunction and a disease of astronomical burden. Septic
shock, the most severe complication of sepsis, leads to a mortality rate as high as 50 …

Car-following (CF) behavior is one of the most important driving behaviors. Accurately
understanding and modeling CF behavior is essential for traffic flow simulation and user …