A Variational Infinite Mixture for Probabilistic Inverse Dynamics Learning. - Hany Abdulsamad; Peter Nickl; Pascal Klink; Jan Peters 001 (ICRA, 2021)

Variational Hierarchical Mixtures for Learning Probabilistic Inverse Dynamics. - Hany Abdulsamad; Peter Nickl; Pascal Klink; Jan Peters 001 (CoRR, 2022)

The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data. - Peter Nickl; Lu Xu; Dharmesh Tailor; Thomas Möllenhoff; Mohammad Emtiyaz Khan (NeurIPS, 2023)

Variational Learning is Effective for Large Deep Networks. - Yuesong Shen; Nico Daheim; Bai Cong; Peter Nickl; Gian Maria Marconi; Clement Bazan; Rio Yokota; Iryna Gurevych; Daniel Cremers; Mohammad Emtiyaz Khan; Thomas Möllenhoff (ICML, 2024)

Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics. - Hany Abdulsamad; Peter Nickl; Pascal Klink; Jan Peters 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2024)