Safe Exploration in Finite Markov Decision Processes with Gaussian Processes. - Matteo Turchetta; Felix Berkenkamp; Andreas Krause 001 (NIPS, 2016)

Safe Model-based Reinforcement Learning with Stability Guarantees. - Felix Berkenkamp; Matteo Turchetta; Angela P. Schoellig; Andreas Krause 001 (NIPS, 2017)

Learning-Based Model Predictive Control for Safe Exploration. - Torsten Koller; Felix Berkenkamp; Matteo Turchetta; Andreas Krause 001 (CDC, 2018)

Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning. - Torsten Koller; Felix Berkenkamp; Matteo Turchetta; Andreas Krause 001 (CoRR, 2018)

Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrations. - Mark Pfeiffer; Samarth Shukla; Matteo Turchetta; Cesar Cadena 001; Andreas Krause 001; Roland Siegwart; Juan I. Nieto 001 (CoRR, 2018)

Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations. - Mark Pfeiffer; Samarth Shukla; Matteo Turchetta; Cesar Cadena 001; Andreas Krause 001; Roland Siegwart; Juan I. Nieto 001 (IEEE Robotics Autom. Lett., 2018)

Safe Exploration for Interactive Machine Learning. - Matteo Turchetta; Felix Berkenkamp; Andreas Krause 001 (NeurIPS, 2019)

Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning. - Torsten Koller; Felix Berkenkamp; Matteo Turchetta; Joschka Boedecker; Andreas Krause 001 (CoRR, 2019)

Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization. - Matteo Turchetta; Andreas Krause 001; Sebastian Trimpe (ICRA, 2020)

Mixed-Variable Bayesian Optimization. - Erik A. Daxberger; Anastasia Makarova; Matteo Turchetta; Andreas Krause 001 (IJCAI, 2020)

Safe Reinforcement Learning via Curriculum Induction. - Matteo Turchetta; Andrey Kolobov; Shital Shah; Andreas Krause 001; Alekh Agarwal (NeurIPS, 2020)

GoSafe: Globally Optimal Safe Robot Learning. - Dominik Baumann; Alonso Marco; Matteo Turchetta; Sebastian Trimpe (ICRA, 2021)

Safe and Efficient Model-free Adaptive Control via Bayesian Optimization. - Christopher König; Matteo Turchetta; John Lygeros; Alisa Rupenyan; Andreas Krause 001 (ICRA, 2021)

Information Directed Reward Learning for Reinforcement Learning. - David Lindner; Matteo Turchetta; Sebastian Tschiatschek; Kamil Ciosek; Andreas Krause 001 (NeurIPS, 2021)

Safety and Robustness in Reinforcement Learning. - Matteo Turchetta (2021)

Near-Optimal Multi-Agent Learning for Safe Coverage Control. - Manish Prajapat; Matteo Turchetta; Melanie N. Zeilinger; Andreas Krause 001 (NeurIPS, 2022)

Learning Long-Term Crop Management Strategies with CyclesGym. - Matteo Turchetta; Luca Corinzia; Scott Sussex; Amanda Burton; Juan Herrera; Ioannis Athanasiadis; Joachim M. Buhmann; Andreas Krause 001 (NeurIPS, 2022)

Scalable Safe Exploration for Global Optimization of Dynamical Systems. - Bhavya Sukhija; Matteo Turchetta; David Lindner; Andreas Krause 001; Sebastian Trimpe; Dominik Baumann (CoRR, 2022)

GoSafeOpt: Scalable safe exploration for global optimization of dynamical systems. - Bhavya Sukhija; Matteo Turchetta; David Lindner; Andreas Krause 001; Sebastian Trimpe; Dominik Baumann (Artif. Intell., 2023)

ChromaX: a fast and scalable breeding program simulator. - Omar G. Younis; Matteo Turchetta; Daniel Ariza Suarez; Steven Yates; Bruno Studer; Ioannis N. Athanasiadis; Andreas Krause 001; Joachim M. Buhmann; Luca Corinzia (Bioinform., 2023)

Safe Guaranteed Exploration for Non-linear Systems. - Manish Prajapat; Johannes Köhler 001; Matteo Turchetta; Andreas Krause 001; Melanie N. Zeilinger (CoRR, 2024)

Breeding Programs Optimization with Reinforcement Learning. - Omar G. Younis; Luca Corinzia; Ioannis N. Athanasiadis; Andreas Krause 001; Joachim M. Buhmann; Matteo Turchetta (CoRR, 2024)

Safe Guaranteed Exploration for Nonlinear Systems. - Manish Prajapat; Johannes Köhler 001; Matteo Turchetta; Andreas Krause 001; Melanie N. Zeilinger (IEEE Trans. Autom. Control., 2025)