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87 Publications
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2025 | Submitted | Preprint | IST-REx-ID: 19674 | 
Basile, Lorenzo, Valentino Maiorca, Luca Bortolussi, Emanuele Rodolà, and Francesco Locatello. “ResiDual Transformer Alignment with Spectral Decomposition.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2411.00246.
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2025 | Published | Conference Paper | IST-REx-ID: 20032 |
Chen, Jiale, Dingling Yao, Adeel A Pervez, Dan-Adrian Alistarh, and Francesco Locatello. “Scalable Mechanistic Neural Networks.” In 13th International Conference on Learning Representations, 63716–37. OpenReview, 2025.
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2024 | Published | Conference Paper | IST-REx-ID: 14213 |
Lao, Dong, Zhengyang Hu, Francesco Locatello, Yanchao Yang, and Stefano Soatto. “Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots.” In 1st Conference on Parsimony and Learning, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 18114 |
Pervez, Adeel A, Francesco Locatello, and Efstratios Gavves. “Mechanistic Neural Networks for Scientific Machine Learning.” In Proceedings of the 41st International Conference on Machine Learning, 235:40484–501. ML Research Press, 2024.
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2024 | Research Data Reference | IST-REx-ID: 18895 |
Cadei, Riccardo, Francesco Locatello, Sylvia Cremer, Lukas Lindorfer, and Cordelia Schmid. “ISTAnt.” Institute of Science and Technology Austria, 2024. https://doi.org/10.6084/M9.FIGSHARE.26484934.V2.
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2024 | Published | Conference Paper | IST-REx-ID: 18956 |
Demirel, Berker, and Huseyin Ozkan. “Decompl: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image.” In 2024 IEEE International Conference on Image Processing, 977–83. IEEE, 2024. https://doi.org/10.1109/icip51287.2024.10647499.
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2024 | Published | Conference Paper | IST-REx-ID: 18964 |
Fan, Ke, Zechen Bai, Tianjun Xiao, Tong He, Max Horn, Yanwei Fu, Francesco Locatello, and Zheng Zhang. “Adaptive Slot Attention: Object Discovery with Dynamic Slot Number.” In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 2024. https://doi.org/10.1109/cvpr52733.2024.02176.
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2024 | Published | Conference Paper | IST-REx-ID: 18971 |
Arefin, Rifat, Yan Zhang, Aristide Baratin, Francesco Locatello, Irina Rish, Dianbo Liu, and Kenji Kawaguchi. “Unsupervised Concept Discovery Mitigates Spurious Correlations.” In Proceedings of the 41st International Conference on Machine Learning, 235:1672–88. ML Research Press, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 14946 |
Yao, Dingling, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, and Francesco Locatello. “Multi-View Causal Representation Learning with Partial Observability.” In 12th International Conference on Learning Representations. Curran Associates, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 19010 |
Yao, Dingling, Dario Rancati, Riccardo Cadei, Marco Fumero, and Francesco Locatello. “Unifying Causal Representation Learning with the Invariance Principle.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 19007 |
Kori, Avinash, Francesco Locatello, Ainkaran Santhirasekaram, Francesca Toni, Ben Glocker, and Fabio De Sousa Ribeiro. “Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 19515 |
Fumero, Marco, Marco Pegoraro, Valentino Maiorca, Francesco Locatello, and Emanuele Rodolà. “Latent Functional Maps: A Spectral Framework for Representation Alignment.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 19517 |
Crisostomi, Donato, Marco Fumero, Daniele Baieri, Florian Bernard, and Emanuele Rodolà. “C2M3: Cycle-Consistent Multi-Model Merging.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 18996 |
Chen, Tianyu, Kevin Bello, Francesco Locatello, Bryon Aragam, and Pradeep Kumar Ravikumar. “Identifying General Mechanism Shifts in Linear Causal Representations.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 19005 |
Yao, Dingling, Caroline J Muller, and Francesco Locatello. “Marrying Causal Representation Learning with Dynamical Systems for Science.” In 38th Conference on Neural Information Processing Systems, Vol. 37. Neural Information Processing Systems Foundation, 2024.
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2024 | Published | Conference Paper | IST-REx-ID: 18847 |
Cadei, Riccardo, Lukas Lindorfer, Sylvia Cremer, Cordelia Schmid, and Francesco Locatello. “Smoke and Mirrors in Causal Downstream Tasks.” In ICML 2024 Workshop AI4Science, Vol. 38. Curran Associates, 2024.
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2023 | Published | Conference Paper | IST-REx-ID: 14105 |
Sinha, Samarth, Peter Gehler, Francesco Locatello, and Bernt Schiele. “TeST: Test-Time Self-Training under Distribution Shift.” In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision. Institute of Electrical and Electronics Engineers, 2023. https://doi.org/10.1109/wacv56688.2023.00278.
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2023 | Submitted | Preprint | IST-REx-ID: 14207 |
Löwe, Sindy, Phillip Lippe, Francesco Locatello, and Max Welling. “Rotating Features for Object Discovery.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2306.00600.
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2023 | Published | Conference Paper | IST-REx-ID: 14208 |
Zhu, Zhenyu, Fanghui Liu, Grigorios G Chrysos, Francesco Locatello, and Volkan Cevher. “Benign Overfitting in Deep Neural Networks under Lazy Training.” In Proceedings of the 40th International Conference on Machine Learning, 202:43105–28. ML Research Press, 2023.
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2023 | Submitted | Preprint | IST-REx-ID: 14209 |
Burg, Max F., Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, and Chris Russell. “A Data Augmentation Perspective on Diffusion Models and Retrieval.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2304.10253.
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