- Friedrich, F., Stammer, W., Schramowski, P., & Kersting, K. (2023): A typology for exploring the mitigation of shortcut behaviour. Nature Machine Intelligence 5:319-330.
- Mitchell, R., Menzenbach, R., Kersting, K., & Mundt, M. (2023). Self-Expanding Neural Networks (Version 3, 2024). arXiv. https://doi.org/10.48550/ARXIV.2307.04526
- Delfosse, Q., Sztwiertnia, S., Rothermel, M., Stammer, W., & Kersting, K. (2024). Interpretable concept bottlenecks to align reinforcement learning agents (SCoBots). Advances in Neural Information Processing Systems, 37, 66826-66855.
- Steinmann, D., Stammer, W., Friedrich, F., & Kersting, K. (2024). Learning to intervene on concept bottlenecks (CB2M). Proceedings of the 41st International Conference on Machine Learning (ICML'24), Vol. 235. JMLR.org, Article 1894, 46556–46571.
- Busch, F. P., Kamath, R. R., Mitchell, R., Stammer, W., Kersting, K., & Mundt, M. (2025). Where is the Truth? The Risk of Getting Confounded in a Continual World (ConCon Dataset). Proceedings of the Forty-second International Conference on Machine Learning (ICML 2025).
- Kamath, R. R., Mitchell, R., Paul, S., Kersting, K., & Mundt, M. (2025). BOWL: A Deceptively Simple Open World Learner. 4th Conference on Lifelong Learning Agents (CoLLAs). https://doi.org/10.48550/arXiv.2402.04814
- Mitchell, R., Alliegro, A., Camoriano, R., Carrión-Ojeda, D., Carta, A., Chalvatzaki, G., … & Mundt, M. (2025). Continual Learning Should Move Beyond Incremental Classification. arXiv preprint arXiv:2502.11927.
- Mitchell, R., & Kersting, K. (2025). Multipole Semantic Attention: A Fast Approximation of Softmax Attention for Pretraining (No. arXiv:2509.10406). arXiv. https://doi.org/10.48550/arXiv.2509.10406
- Steinmann, D., Stammer, W., Wüst, A., & Kersting, K. (2025). Object Centric Concept Bottlenecks (OCB) (No. arXiv:2505.24492). arXiv. https://doi.org/10.48550/arXiv.2505.24492
Hier finden Sie eine umfassende Auflistung der WhiteBox-Publikationen
