Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems. - Andreas Schlaginhaufen; Philippe Wenk; Andreas Krause 001; Florian Dörfler (NeurIPS, 2021)

Identifiability and Generalizability in Constrained Inverse Reinforcement Learning. - Andreas Schlaginhaufen; Maryam Kamgarpour (ICML, 2023)

Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm. - Titouan Renard; Andreas Schlaginhaufen; Tingting Ni; Maryam Kamgarpour (CDC, 2024)

Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning. - Andreas Schlaginhaufen; Maryam Kamgarpour (NeurIPS, 2024)

Efficient Preference-Based Reinforcement Learning: Randomized Exploration Meets Experimental Design. - Andreas Schlaginhaufen; Reda Ouhamma; Maryam Kamgarpour (CoRR, 2025)