Second-Moment Loss: A Novel Regression Objective for Improved Uncertainties. - Joachim Sicking; Maram Akila; Maximilian Pintz; Tim Wirtz; Asja Fischer; Stefan Wrobel (CoRR, 2020)

A Novel Regression Loss for Non-Parametric Uncertainty Optimization. - Joachim Sicking; Maram Akila; Maximilian Pintz; Tim Wirtz; Asja Fischer; Stefan Wrobel (CoRR, 2021)

DenseHMM: Learning Hidden Markov Models by Learning Dense Representations. - Joachim Sicking; Maximilian Pintz; Maram Akila; Tim Wirtz (ICPRAM, 2022)

A Survey on Uncertainty Toolkits for Deep Learning. - Maximilian Pintz; Joachim Sicking; Maximilian Poretschkin; Maram Akila (CoRR, 2022)

PARMA: a Platform Architecture to enable Automated, Reproducible, and Multi-party Assessments of AI Trustworthiness. - Maximilian Pintz; Daniel Becker 010; Michael Mock (RAIE@ICSE, 2024)

Wasserstein dropout. - Joachim Sicking; Maram Akila; Maximilian Pintz; Tim Wirtz; Stefan Wrobel; Asja Fischer (Mach. Learn., 2024)

Navigating the Landscape of AI Test Methods Using Taxonomy-Based Selection. - Maximilian Pintz; Anna Schmitz 001; Rebekka Görge; Sebastian Schmidt; Daniel Becker 010; Maram Akila; Michael Mock (RAIE, 2025)

A Modular AI Testing Framework for Trustworthy AI: Proof-of-Concept Implementation. - Maximilian Pintz; Daniel Becker 010; Michael Mock (SAFECOMP (Workshops), 2025)