Training for Implicit Norms in Deep Reinforcement Learning Agents through Adversarial Multi-Objective Reward Optimization. - Markus Peschl (AIES, 2021)

MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning. - Markus Peschl; Arkady Zgonnikov; Frans A. Oliehoek; Luciano Cavalcante Siebert (AAMAS, 2022)

Learning Perturbations for Soft-Output Linear MIMO Demappers. - Daniel E. Worrall; Markus Peschl; Arash Behboodi; Roberto Bondesan (GLOBECOM, 2022)

Robust Scheduling with GFlowNets. - David W. Zhang; Corrado Rainone; Markus Peschl; Roberto Bondesan (ICLR, 2023)

Probabilistic and Differentiable Wireless Simulation with Geometric Transformers. - Thomas Hehn 001; Markus Peschl; Tribhuvanesh Orekondy; Arash Behboodi; Johann Brehmer (CoRR, 2024)

NeuroSteiner: A Graph Transformer for Wirelength Estimation. - Sahil Manchanda; Dana Kianfar; Markus Peschl; Romain Lepert; Michaël Defferrard (CoRR, 2024)

Differentiable and Learnable Wireless Simulation with Geometric Transformers. - Thomas Hehn 001; Markus Peschl; Tribhuvanesh Orekondy; Arash Behboodi; Johann Brehmer (ICLR, 2025)