ResiDual transformer alignment with spectral decomposition
L. Basile, V. Maiorca, L. Bortolussi, E. Rodolà, F. Locatello, ArXiv (n.d.).

Near, far: Patch-ordering enhances vision foundation models' scene understanding
V. Pariza, M. Salehi, G. Burghouts, F. Locatello, Y.M. Asano, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 72303–72330.

Scalable mechanistic neural networks
J. Chen, D. Yao, A.A. Pervez, D.-A. Alistarh, F. Locatello, in:, 13th International Conference on Learning Representations, ICLR, 2025, pp. 63716–63737.

Sparse causal effect estimation using two-sample summary statistics in the presence of unmeasured confounding
S. Huang, N. Pfister, J. Bowden, in:, The 28th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2025, pp. 3394–3402.

Propagating model uncertainty through filtering-based probabilistic numerical ODE solvers
D. Yao, F. Tronarp, N. Bosch, in:, Proceedings of the 1st International Conference on Probabilistic Numerics, ML Research Press, 2025.

Divided attention: Unsupervised multi-object discovery with contextually separated slots
D. Lao, Z. Hu, F. Locatello, Y. Yang, S. Soatto, in:, 1st Conference on Parsimony and Learning, 2024.

Mechanistic neural networks for scientific machine learning
A.A. Pervez, F. Locatello, E. Gavves, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 40484–40501.

ISTAnt
R. Cadei, F. Locatello, S. Cremer, L. Lindorfer, C. Schmid, (2024).

Unsupervised concept discovery mitigates spurious correlations
R. Arefin, Y. Zhang, A. Baratin, F. Locatello, I. Rish, D. Liu, K. Kawaguchi, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 1672–1688.

Multi-view causal representation learning with partial observability
D. Yao, D. Xu, S. Lachapelle, S. Magliacane, P. Taslakian, G. Martius, J. von Kügelgen, F. Locatello, in:, 12th International Conference on Learning Representations, Curran Associates, 2024.

Unifying causal representation learning with the invariance principle
D. Yao, D. Rancati, R. Cadei, M. Fumero, F. Locatello, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.

Identifiable object-centric representation learning via probabilistic slot attention
A. Kori, F. Locatello, A. Santhirasekaram, F. Toni, B. Glocker, F. De Sousa Ribeiro, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.

Latent functional maps: A spectral framework for representation alignment
M. Fumero, M. Pegoraro, V. Maiorca, F. Locatello, E. Rodolà, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.

M3: Cycle-consistent multi-model merging
D. Crisostomi, M. Fumero, D. Baieri, F. Bernard, E. Rodolà, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.

Identifying general mechanism shifts in linear causal representations
T. Chen, K. Bello, F. Locatello, B. Aragam, P.K. Ravikumar, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.

Marrying causal representation learning with dynamical systems for science
D. Yao, C.J. Muller, F. Locatello, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.

Smoke and mirrors in causal downstream tasks
R. Cadei, L. Lindorfer, S. Cremer, C. Schmid, F. Locatello, in:, ICML 2024 Workshop AScience, Curran Associates, 2024.

Decompl: Decompositional learning with attention pooling for group activity recognition from a single volleyball image
B. Demirel, H. Ozkan, in:, 2024 IEEE International Conference on Image Processing, IEEE, 2024, pp. 977–983.

Adaptive slot attention: Object discovery with dynamic slot number
K. Fan, Z. Bai, T. Xiao, T. He, M. Horn, Y. Fu, F. Locatello, Z. Zhang, in:, 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2024.

TeST: Test-time Self-Training under distribution shift
S. Sinha, P. Gehler, F. Locatello, B. Schiele, in:, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2023.