A Lederer, AJO Conejo, KA Maier… - International …, 2021 - proceedings.mlr.press
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 …
S Stanton, W Maddox, I Delbridge… - International …, 2021 - proceedings.mlr.press
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 …
G Xie, X Chen - Journal of Aerospace Information Systems, 2022 - arc.aiaa.org
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 …
V Gómez-Verdejo… - … on Neural Networks …, 2023 - ieeexplore.ieee.org
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 …