Abstract International Roughness Index (IRI) is an important pavement performance
indicator that is widely used to reflect existing pavement condition and ride quality. Due to …

The safe operation of physical systems typically relies on high-quality models. Since a
continuous stream of data is generated during run-time, such models are often obtained …

In the past decade, semi-supervised learning (SSL) over graphs has gained popularity due
to its importance in a gamut of network science applications. While most of existing SSL …

Advanced computational methods are being actively sought to address the challenges
associated with the discovery and development of new combinatorial materials, such as …

Gaussian processes (GPs) provide a gold standard for performance in online settings, such
as sample-efficient control and black box optimization, where we need to update a posterior …

As an important type of dynamic data-driven application system, unmanned aerial vehicles
(UAVs) are widely used for civilian, commercial, and military applications across the globe …

Abstract Model-based approaches to reinforcement learning (MBRL) exhibit favorable
performance in practice, but their theoretical guarantees in large spaces are mostly …

Adaptive learning is necessary for nonstationary environments where the learning machine
needs to forget past data distribution. Efficient algorithms require a compact model update to …

Directed Exploration is a crucial challenge in reinforcement learning (RL), especially when
rewards are sparse. Information-directed sampling (IDS), which optimizes the information …

The increased demand for online prediction and the growing availability of large data sets
drives the need for computationally efficient models. While exact Gaussian process …