Behavior Constraining in Weight Space for Offline Reinforcement Learning. - Phillip Swazinna; Steffen Udluft; Daniel Hein 001; Thomas A. Runkler (ESANN, 2021)

Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning. - Phillip Swazinna; Steffen Udluft; Thomas A. Runkler (SSCI, 2021)

Overcoming model bias for robust offline deep reinforcement learning. - Phillip Swazinna; Steffen Udluft; Thomas A. Runkler (Eng. Appl. Artif. Intell., 2021)

Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning. - Phillip Swazinna; Steffen Udluft; Daniel Hein 001; Thomas A. Runkler (CoRR, 2022)

Automatic Trade-off Adaptation in Offline RL. - Phillip Swazinna; Steffen Udluft; Thomas A. Runkler (ESANN, 2023)

User-Interactive Offline Reinforcement Learning. - Phillip Swazinna; Steffen Udluft; Thomas A. Runkler (ICLR, 2023)

Learning Control Policies for Variable Objectives from Offline Data. - Marc Weber; Phillip Swazinna; Daniel Hein 001; Steffen Udluft; Volkmar Sterzing (SSCI, 2023)

Policy Regularization for Model-Based Offline Reinforcement Learning (Policy Regularisierung für Modellbasiertes Offline Reinforcement Learning) - Phillip Swazinna (2023)

Iterative Batch Reinforcement Learning via Safe Diversified Model-based Policy Search. - Amna Najib; Stefan Depeweg; Phillip Swazinna (CoRR, 2024)

TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning. - Batikan Bora Ormanci; Phillip Swazinna; Steffen Udluft; Thomas A. Runkler (CoRR, 2024)

Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications. - Jean-Philippe Corbeil; Asma Ben Abacha; George Michalopoulos; Phillip Swazinna; Miguel A. del Agua; Jérôme Tremblay; Akila Jeeson Daniel; Cari Bader; Yu-Cheng Cho; Pooja Krishnan; Nathan Bodenstab; Thomas Lin; et al. (CoRR, 2025)