Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration. - Christian Tomani; Florian Buettner 001 (AAAI, 2021)

Post-Hoc Uncertainty Calibration for Domain Drift Scenarios. - Christian Tomani; Sebastian Gruber 001; Muhammed Ebrar Erdem; Daniel Cremers; Florian Buettner 001 (CVPR, 2021)

Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration. - Christian Tomani; Daniel Cremers; Florian Buettner 001 (ECCV (13), 2022)

What Makes Graph Neural Networks Miscalibrated? - Hans Hao-Hsun Hsu; Yuesong Shen; Christian Tomani; Daniel Cremers (NeurIPS, 2022)

CHALLENGER: Training with Attribution Maps. - Christian Tomani; Daniel Cremers (CoRR, 2022)

Beyond In-Domain Scenarios: Robust Density-Aware Calibration. - Christian Tomani; Futa Kai Waseda; Yuesong Shen; Daniel Cremers (ICML, 2023)

Quality Control at Your Fingertips: Quality-Aware Translation Models. - Christian Tomani; David Vilar; Markus Freitag; Colin Cherry; Subhajit Naskar; Mara Finkelstein; Daniel Cremers (CoRR, 2023)

Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model. - Christian Tomani; David Vilar; Markus Freitag; Colin Cherry; Subhajit Naskar; Mara Finkelstein; Xavier Garcia; Daniel Cremers (ACL (1), 2024)

Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations. - Christian Tomani; Kamalika Chaudhuri; Ivan Evtimov; Daniel Cremers; Mark Ibrahim (CoRR, 2024)