Predictive monitoring of black-box dynamical systems
T.A. Henzinger, F. Kresse, K. Mallik, E. Yu, D. Zikelic, in:, 7th Annual Learning for Dynamics &; Control Conference, ML Research Press, 2025, pp. 804–816.

Logic gate neural networks are good for verification
F. Kresse, E. Yu, C. Lampert, T.A. Henzinger, in:, 2nd International Conferenceon Neuro-Symbolic Systems, ML Research Press, 2025.

Efficient estimation of a Gaussian mean with local differential privacy
N. Kalinin, L. Steinberger, in:, Proceedings of the 28th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2025, pp. 118–126.

Robust image classification with 1-Lipschitz networks
B. Prach, Robust Image Classification with 1-Lipschitz Networks, Institute of Science and Technology Austria, 2025.

Intriguing properties of robust classification
B. Prach, C. Lampert, in:, 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, IEEE, 2025, pp. 660–669.

Communication-efficient federated learning with data and client heterogeneity
H. Zakerinia, S. Talaei, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2024, pp. 3448–3456.

PEFLL: Personalized federated learning by learning to learn
J.A. Scott, H. Zakerinia, C. Lampert, in:, 12th International Conference on Learning Representations, OpenReview, 2024.

More flexible PAC-Bayesian meta-learning by learning learning algorithms
H. Zakerinia, A. Behjati, C. Lampert, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 58122–58139.

Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials
J.A. Scott, Á. Cahill, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 44012–44037.

Generalization in multi-objective machine learning
P. Súkeník, C. Lampert, Neural Computing and Applications (2024).

Can LLMs separate instructions from data? And what do we even mean by that?
E. Zverev, S. Abdelnabi, S. Tabesh, M. Fritz, C. Lampert, ArXiv (2024).

Continual learning: Applications and the road forward
E. Verwimp, R. Aljundi, S. Ben-David, M. Bethge, A. Cossu, A. Gepperth, T.L. Hayes, E. Hüllermeier, C. Kanan, D. Kudithipudi, C. Lampert, M. Mundt, R. Pascanu, A. Popescu, A.S. Tolias, J. Van De Weijer, B. Liu, V. Lomonaco, T. Tuytelaars, G.M. Van De Ven, Transactions on Machine Learning Research 2024 (2024).

Banded square root matrix factorization for differentially private model training
N. Kalinin, C. Lampert, in:, 38th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.

Neural collapse versus low-rank bias: Is deep neural collapse really optimal?
P. Súkeník, C. Lampert, M. Mondelli, in:, 38th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.

Predicting the geolocation of tweets using transformer models on customized data
K. Lutsai, C. Lampert, Journal of Spatial Information Science (2024) 69–99.

1-Lipschitz layers compared: Memory, speed, and certifiable robustness
B. Prach, F. Brau, G. Buttazzo, C. Lampert, in:, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Computer Vision Foundation, 2024, pp. 24574–24583.

Intriguing properties of robust classification
B. Prach, C. Lampert, ArXiv (n.d.).

Deep learning extraction of band structure parameters from density of states: A case study on trilayer graphene
P.M. Henderson, A. Ghazaryan, A.A. Zibrov, A.F. Young, M. Serbyn, Physical Review B 108 (2023).

On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift
P. Tomaszewska, C. Lampert, in:, International Workshop on Reproducible Research in Pattern Recognition, Springer Nature, 2023, pp. 67–73.

Cross-client label propagation for transductive and semi-supervised federated learning
J.A. Scott, M.X. Yeo, C. Lampert, in:, Transactions in Machine Learning, Curran Associates, 2023.