Alexander Meinke

PhD thesis, 2023

Tell, don't show: Declarative facts influence how LLMs generalize.

[BibT_eX]

[DOI]

Owain Evans

CoRR, 2023

Improving Fairness and Cybersecurity in the Artificial Intelligence Act.

[BibT_eX]

[DOI]

Gabriele Carovano

Proceedings of the 2nd European Workshop on Algorithmic Fairness, 2023

2022

Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

2021

Provably Robust Detection of Out-of-distribution Data (almost) for free.

[BibT_eX]

[DOI]

CoRR, 2021

2020

Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data.

[BibT_eX]

[DOI]

CoRR, 2020

Certifiably Adversarially Robust Detection of Out-of-Distribution Data.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Towards neural networks that provably know when they don't know.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

Adversarial Robustness on In- and Out-Distribution Improves Explainability.

[BibT_eX]

[DOI]

Maximilian Augustin