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).

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.

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.

Identifying general mechanism shifts in linear causal representations

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.

Unifying causal representation learning with the invariance principle

Marrying causal representation learning with dynamical systems for science

Identifiable object-centric representation learning via probabilistic slot attention

Latent functional maps: A spectral framework for representation alignment

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.

Rotating features for object discovery
S. Löwe, P. Lippe, F. Locatello, M. Welling, ArXiv (n.d.).

Benign overfitting in deep neural networks under lazy training
Z. Zhu, F. Liu, G.G. Chrysos, F. Locatello, V. Cevher, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 43105–43128.

A data augmentation perspective on diffusion models and retrieval
M.F. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, ArXiv (n.d.).

Leveraging sparse and shared feature activations for disentangled representation learning
M. Fumero, F. Wenzel, L. Zancato, A. Achille, E. Rodolà, S. Soatto, B. Schölkopf, F. Locatello, ArXiv (n.d.).

Causal discovery with score matching on additive models with arbitrary noise
F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.

Scalable causal discovery with score matching
F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.

Causal triplet: An open challenge for intervention-centric causal representation learning
Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.