| Open problems and fundamental limitations of reinforcement learning from human feedback S Casper, X Davies, C Shi, TK Gilbert, J Scheurer, J Rando, R Freedman, ... arXiv preprint arXiv:2307.15217, 2023 | 144 | 2023 |
| Artificial intelligence in dermatology: a primer AT Young, M Xiong, J Pfau, MJ Keiser, ML Wei Journal of Investigative Dermatology 140 (8), 1504-1512, 2020 | 143 | 2020 |
| Goal misgeneralization in deep reinforcement learning LL Di Langosco, J Koch, LD Sharkey, J Pfau, D Krueger International Conference on Machine Learning, 12004-12019, 2022 | 68 | 2022 |
| Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models AT Young, K Fernandez, J Pfau, R Reddy, NA Cao, MY von Franque, ... NPJ digital medicine 4 (1), 10, 2021 | 29 | 2021 |
| Artificial intelligence in teledermatology M Xiong, J Pfau, AT Young, ML Wei Current Dermatology Reports 8, 85-90, 2019 | 16 | 2019 |
| Robust Semantic Interpretability: Revisiting Concept Activation Vectors J Pfau, A Young, J Wei, M Wei, M Keiser arXiv preprint arXiv:2104.02768, 2020 | 10 | 2020 |
| Global saliency: aggregating saliency maps to assess dataset artefact bias J Pfau, AT Young, ML Wei, MJ Keiser arXiv preprint arXiv:1910.07604, 2019 | 9 | 2019 |
| Objective robustness in deep reinforcement learning J Koch, L Langosco, J Pfau, J Le, L Sharkey arXiv preprint arXiv:2105.14111 2, 2021 | 8 | 2021 |
| Eliciting language model behaviors using reverse language models J Pfau, A Infanger, A Sheshadri, A Panda, J Michael, C Huebner Socially Responsible Language Modelling Research, 2023 | 5 | 2023 |
| Self-Consistency of Large Language Models under Ambiguity H Bartsch, O Jorgensen, D Rosati, J Hoelscher-Obermaier, J Pfau arXiv preprint arXiv:2310.13439, 2023 | | 2023 |
