On the identifiability of causal graphs with multiple environments
F. Montagna, in:, The 14th International Conference on Learning Representations, OpenReview, n.d.

Demystifying amortized causal discovery with transformers
F. Montagna, M.T. Cairney-Leeming, D. Sridhar, F. Locatello, Transactions on Machine Learning Research (2025).

How to probe: Simple yet effective techniques for improving post-hoc explanations
S. Gairola, M. Böhle, F. Locatello, B. Schiele, in:, 13th International Conference on Learning Representations, ICLR, 2025.

Score matching through the roof: Linear, nonlinear, and latent variables causal discovery
F. Montagna, P. Faller, P. Blöbaum, E. Kirschbaum, F. Locatello, in:, Proceedings of the Fourth Conference on Causal Learning and Reasoning, ML Research Press, 2025, pp. 552–605.

Unifying causal representation learning with the invariance principle
D. Yao, D. Rancati, R. Cadei, M. Fumero, F. Locatello, in:, 13th International Conference on Learning Representations, ICLR, 2025.

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.

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.

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.

Connecting neural models latent geometries with relative geodesic representations
H. Yu, B. Inal, G. Arvanitidis, S. Hauberg, F. Locatello, M. Fumero, in:, 39th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2025.

Head pursuit: Probing attention specialization in multimodal transformers
L. Basile, V. Maiorca, D. Doimo, F. Locatello, A. Cazzaniga, in:, 39th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2025.

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

Mechanistic PDE networks for discovery of governing equations
A.A. Pervez, E. Gavves, F. Locatello, in:, 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 48962–48973.

Prediction-powered causal inferences
R. Cadei, I. Demirel, P. De Bartolomeis, L. Lindorfer, S. Cremer, C. Schmid, F. Locatello, in:, 39th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2025.

The third pillar of causal analysis? A measurement perspective on causal representations
D. Yao, S. Huang, R. Cadei, K. Zhang, F. Locatello, in:, 39th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2025.

Out-of-Distribution detection with relative angles
B. Demirel, M. Fumero, F. Locatello, in:, 39th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2025.

Prediction-Powered Causal Inferences
R. Cadei, I. Demirel, P. De Bartolomeis, L. Lindorfer, S. Cremer, C. Schmid, F. Locatello, in:, 39th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2025, pp. 82200–82229.

Connecting neural models latent geometries with relative geodesic representations
H. Yu, B. Inal, G. Arvanitidis, S. Hauberg, F. Locatello, M. Fumero, in:, 39th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2025, pp. 125316–125360.

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.

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.