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@inproceedings{tomani2021falcon,
 author = {C Tomani and F Buettner},
 title = {Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration},
 booktitle = {InThirty-FifthAAAIConferenceonArtificialIntelligence(AAAI-2021)},
 year = {2021},
 eprint = {2012.10923},
 eprinttype = {arXiv},
 keywords = {deep learning},
}

@inproceedings{tomani2021posthoc,
 author = {C Tomani and S Gruber and ME Erdem and D Cremers and F Buettner},
 title = {Post-hoc Uncertainty Calibration for Domain Drift Scenarios},
 booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 year = {2021},
 award = {Oral Presentation},
 eprint = {2012.10988},
 eprinttype = {arXiv},
 keywords = {deep learning},
}

@inproceedings{tomani2021pts,
 title = {Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration},
 author = {C Tomani and D Cremers and F Buettner},
 year = {2022},
 booktitle = {European Conference on Computer Vision (ECCV)},
 eprint = {2102.12182},
 eprinttype = {arXiv},
 keywords = {deep learning},
}

@article{tomani2022challenger,
 title = {Challenger: Training with Attribution Maps},
 author = {C Tomani and D Cremers},
 year = {2022},
 journal = {arXiv preprint},
 eprint = {2205.15094},
 eprinttype = {arXiv},
 primaryclass = {cs.LG},
 keywords = {deep learning},
}

@inproceedings{tomani2023dac,
 title = {Beyond In-Domain Scenarios: Robust Density-Aware Calibration},
 author = {C Tomani and F Waseda and Y Shen and D Cremers},
 booktitle = {Proceedings of the 40th International Conference on Machine Learning (ICML)},
 year = {2023},
 eprint = {2302.05118},
 eprinttype = {arXiv},
 keywords = {deep learning},
}

@inproceedings{tomani2023qualityaware,
 title = {Quality-Aware Translation Models: Efficient Generation and Quality
 Estimation in a Single Model},
 author = {C Tomani and D Vilar and M Freitag and C Cherry and S Naskar and M Finkelstein and X Garcia and D Cremers},
 booktitle = {Proceedings of the 62nd Annual Meeting of the Association for
 Computational Linguistics (ACL)},
 year = {2024},
 eprint = {2310.06707},
 eprinttype = {arXiv},
 keywords = {deep learning},
}

@article{tomani2024abstentionllms,
 title = {Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations},
 author = {C Tomani and K Chaudhuri and I Evtimov and D Cremers and M Ibrahim},
 year = {2024},
 journal = {arXiv preprint},
 eprint = {2404.10960},
 eprinttype = {arXiv},
 primaryclass = {cs.LG},
 keywords = {deep learning},
}

@inproceedings{hsu2022gats,
 title = {What Makes Graph Neural Networks Miscalibrated?},
 author = {HHH Hsu and Y Shen and C Tomani and D Cremers},
 booktitle = {NeurIPS},
 year = {2022},
 eprint = {2210.06391},
 eprinttype = {arXiv},
 eprintclass = {cs.LG},
 keywords = {deep learning, graph neural network, calibration},
}