630 citations · 696 across the 22 of their papers we have counts for
9 papers · 1 filter
FedXGBoost: Privacy-Preserving XGBoost for Federated Learning
Nhan Khanh Le, Yang Liu, Quang Minh Nguyen +4
Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy. Practical adaptation of XG…
Inverse Reinforcement Learning: A Control Lyapunov Approach
Samuel Tesfazgi, Armin Lederer, Sandra Hirche
Inferring the intent of an intelligent agent from demonstrations and subsequently predicting its behavior, is a critical task in many collaborative settings. A common approach to s…
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control
Armin Lederer, Jonas Umlauft, Sandra Hirche
In application areas where data generation is expensive, Gaussian processes are a preferred supervised learning model due to their high data-efficiency. Particularly in model-based…
Anticipating the Long-Term Effect of Online Learning in Control
Alexandre Capone, Sandra Hirche
Control schemes that learn using measurement data collected online are increasingly promising for the control of complex and uncertain systems. However, in most approaches of this…
GP3: A Sampling-based Analysis Framework for Gaussian Processes
Armin Lederer, Markus Kessler, Sandra Hirche
Although machine learning is increasingly applied in control approaches, only few methods guarantee certifiable safety, which is necessary for real world applications. These approa…
Localized active learning of Gaussian process state space models
Alexandre Capone, Jonas Umlauft, Thomas Beckers +2
The performance of learning-based control techniques crucially depends on how effectively the system is explored. While most exploration techniques aim to achieve a globally accura…