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 …

Off-policy evaluation (OPE) is important for closing the gap between offline training and
evaluation of reinforcement learning (RL), by estimating performance and/or rank of target …

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

Deep brain stimulation (DBS) has shown great promise toward treating motor symptoms
caused by Parkinson's disease (PD), by delivering electrical pulses to the Basal Ganglia …

Although reinforcement learning (RL) is considered the gold standard for policy design, it
may not always provide a robust solution in various scenarios. This can result in severe …

Real-world Electronic Health Records (EHRs) are often plagued by a high rate of missing
data. In our EHRs, for example, the missing rates can be as high as 90% for some features …

Model-based methods have recently shown great potential for off-policy evaluation (OPE);
offline trajectories induced by behavioral policies are fitted to transitions of Markov decision …

In the realm of Deep Q-Networks (DQNs), numerous exploration strategies have
demonstrated efficacy within controlled environments. However, these methods encounter …

Artificial intelligence (AI) and deep learning (DL) have recently shown success in domains
related to healthcare and its decision-making systems. However, most of the existing …

Although reinforcement learning (RL) is considered the gold standard for policy design, it
may not always provide a robust solution in various scenarios. This can result in severe …