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2020 – today
- 2024
[i9]Rudolf Laine, Bilal Chughtai, Jan Betley, Kaivalya Hariharan, Jérémy Scheurer, Mikita Balesni, Marius Hobbhahn, Alexander Meinke, Owain Evans:
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs. CoRR abs/2407.04694 (2024)- [i8]Mikita Balesni, Marius Hobbhahn, David Lindner, Alexander Meinke, Tomek Korbak, Joshua Clymer, Buck Shlegeris, Jérémy Scheurer, Charlotte Stix, Rusheb Shah, Nicholas Goldowsky-Dill, Dan Braun, Bilal Chughtai, Owain Evans, Daniel Kokotajlo, Lucius Bushnaq:
Towards evaluations-based safety cases for AI scheming. CoRR abs/2411.03336 (2024) - [i7]Alexander Meinke, Bronson Schoen, Jérémy Scheurer, Mikita Balesni, Rusheb Shah, Marius Hobbhahn:
Frontier Models are Capable of In-context Scheming. CoRR abs/2412.04984 (2024) - 2023
- [b1]Alexander Meinke:
Robust Out-of-Distribution Detection in Deep Classifiers. University of Tübingen, Germany, 2023 - [c6]Gabriele Carovano, Alexander Meinke:
Improving Fairness and Cybersecurity in the Artificial Intelligence Act. EWAF 2023 - [i6]Alexander Meinke, Owain Evans:
Tell, don't show: Declarative facts influence how LLMs generalize. CoRR abs/2312.07779 (2023) - 2022
- [c5]Julian Bitterwolf, Alexander Meinke, Maximilian Augustin, Matthias Hein:
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities. ICML 2022: 2041-2074 - [c4]Alexander Meinke, Julian Bitterwolf, Matthias Hein:
Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free. NeurIPS 2022 - [i5]Julian Bitterwolf, Alexander Meinke, Maximilian Augustin, Matthias Hein:
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities. CoRR abs/2206.09880 (2022) - 2021
- [i4]Alexander Meinke, Julian Bitterwolf, Matthias Hein:
Provably Robust Detection of Out-of-distribution Data (almost) for free. CoRR abs/2106.04260 (2021) - 2020
- [c3]Maximilian Augustin, Alexander Meinke, Matthias Hein:
Adversarial Robustness on In- and Out-Distribution Improves Explainability. ECCV (26) 2020: 228-245 - [c2]Alexander Meinke, Matthias Hein:
Towards neural networks that provably know when they don't know. ICLR 2020 - [c1]Julian Bitterwolf, Alexander Meinke, Matthias Hein:
Certifiably Adversarially Robust Detection of Out-of-Distribution Data. NeurIPS 2020 - [i3]Maximilian Augustin, Alexander Meinke, Matthias Hein:
Adversarial Robustness on In- and Out-Distribution Improves Explainability. CoRR abs/2003.09461 (2020) - [i2]Julian Bitterwolf, Alexander Meinke, Matthias Hein:
Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data. CoRR abs/2007.08473 (2020)
2010 – 2019
- 2019
- [i1]Alexander Meinke, Matthias Hein:
Towards neural networks that provably know when they don't know. CoRR abs/1909.12180 (2019)
Coauthor Index
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