2 papers
cs.LG2025
Deep Gaussian Process Proximal Policy Optimization
Matthijs van der Lende, Juan Cardenas-Cartagena
Uncertainty estimation for Reinforcement Learning (RL) is a critical component in control tasks where agents must balance safe exploration and efficient learning. While deep neural…
stat.ML2025
Evaluating Uncertainty in Deep Gaussian Processes
Matthijs van der Lende, Jeremias Lino Ferrao, Niclas Müller-Hof
Reliable uncertainty estimates are crucial in modern machine learning. Deep Gaussian Processes (DGPs) and Deep Sigma Point Processes (DSPPs) extend GPs hierarchically, offering pro…