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.

Sinha, Samarth, Peter Gehler, Francesco Locatello, and Bernt Schiele. “TeST: Test-Time Self-Training under Distribution Shift.”https://doi.org/10.1109/wacv56688.2023.00278__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 .

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.

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.”https://doi.org/10.48550/arXiv.2304.10253__ArXiv, n.d.https://doi.org/10.48550/arXiv.2304.10253 .

Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and Francesco Locatello. “Causal Discovery with Score Matching on Additive Models with Arbitrary Noise.” In 2nd Conference on Causal Learning and Reasoning, 2023.

Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, and Francesco Locatello. “Scalable Causal Discovery with Score Matching.” In 2nd Conference on Causal Learning and Reasoning, 2023.

Liu, Yuejiang, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, and Francesco Locatello. “Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning.” In 2nd Conference on Causal Learning and Reasoning, 2023.

Moschella, Luca, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, and Emanuele Rodolà. “Relative Representations Enable Zero-Shot Latent Space Communication.” In The 11th International Conference on Learning Representations, 2023.

Tangemann, Matthias, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, and Bernhard Schölkopf. “Unsupervised Object Learning via Common Fate.” In 2nd Conference on Causal Learning and Reasoning, 2023.

Seitzer, Maximilian, Max Horn, Andrii Zadaianchuk, Dominik Zietlow, Tianjun Xiao, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Tong He, et al. “Bridging the Gap to Real-World Object-Centric Learning.” In The 11th International Conference on Learning Representations, 2023.

Zadaianchuk, Andrii, Matthaeus Kleindessner, Yi Zhu, Francesco Locatello, and Thomas Brox. “Unsupervised Semantic Segmentation with Self-Supervised Object-Centric Representations.” In The 11th International Conference on Learning Representations, 2023.

Faller, Philipp M., Leena Chennuru Vankadara, Atalanti A. Mastakouri, Francesco Locatello, and Dominik Janzing. “Self-Compatibility: Evaluating Causal Discovery without Ground Truth.”https://doi.org/10.48550/arXiv.2307.09552__ArXiv, n.d.https://doi.org/10.48550/arXiv.2307.09552 .

Burg, Max, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, and Chris Russell. “Image Retrieval Outperforms Diffusion Models on Data Augmentation.” Journal of Machine Learning Research. ML Research Press, 2023.

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.”https://doi.org/10.48550/arXiv.2311.04056__ArXiv, n.d.https://doi.org/10.48550/arXiv.2311.04056 .

Maiorca, Valentino, Luca Moschella, Antonio Norelli, Marco Fumero, Francesco Locatello, and Emanuele Rodolà. “Latent Space Translation via Semantic Alignment.”https://doi.org/10.48550/arXiv.2311.00664__ArXiv, n.d.https://doi.org/10.48550/arXiv.2311.00664 .

Kori, Avinash, Francesco Locatello, Fabio De Sousa Ribeiro, Francesca Toni, and Ben Glocker. “Grounded Object Centric Learning.”https://doi.org/10.48550/arXiv.2307.09437__ArXiv, n.d.https://doi.org/10.48550/arXiv.2307.09437 .

Zhu, Zhenyu, Francesco Locatello, and Volkan Cevher. “Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling.”https://doi.org/10.48550/arXiv.2310.18123__ArXiv, n.d.https://doi.org/10.48550/arXiv.2310.18123 .

Montagna, Francesco, Atalanti A. Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, and Francesco Locatello. “Assumption Violations in Causal Discovery and the Robustness of Score Matching.”https://doi.org/10.48550/arXiv.2310.13387__ArXiv, n.d.https://doi.org/10.48550/arXiv.2310.13387 .

Fumero, Marco, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, and Francesco Locatello. “Leveraging Sparse and Shared Feature Activations for Disentangled Representation Learning.”https://doi.org/10.48550/arXiv.2304.07939__ArXiv, n.d.https://doi.org/10.48550/arXiv.2304.07939 .

Löwe, Sindy, Phillip Lippe, Francesco Locatello, and Max Welling. “Rotating Features for Object Discovery.”https://doi.org/10.48550/arXiv.2306.00600__ArXiv, n.d.https://doi.org/10.48550/arXiv.2306.00600 .

Zhao, Zixu, Jiaze Wang, Max Horn, Yizhuo Ding, Tong He, Zechen Bai, Dominik Zietlow, et al. “Object-Centric Multiple Object Tracking.”https://doi.org/10.48550/arXiv.2309.00233__ArXiv, n.d.https://doi.org/10.48550/arXiv.2309.00233 .

Montagna, Francesco, Nicoletta Noceti, Lorenzo Rosasco, and Francesco Locatello. “Shortcuts for Causal Discovery of Nonlinear Models by Score Matching.”https://doi.org/10.48550/arXiv.2310.14246__ArXiv, n.d.https://doi.org/10.48550/arXiv.2310.14246 .

Fan, Ke, Zechen Bai, Tianjun Xiao, Dominik Zietlow, Max Horn, Zixu Zhao, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, et al. “Unsupervised Open-Vocabulary Object Localization in Videos.”https://doi.org/10.48550/arXiv.2309.09858__ArXiv, n.d.https://doi.org/10.48550/arXiv.2309.09858 .

Xu, Danru, Dingling Yao, Sebastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, and Sara Magliacane. “A Sparsity Principle for Partially Observable Causal Representation Learning.” In Causal Representation Learning Workshop at NeurIPS 2023. OpenReview, 2023.

Zhang, Cheng, Dominik Janzing, Mihaela  van der Schaar, Francesco Locatello, Peter Spirtes, Kun Zhang, Bernhard Schölkopf, and Caroline Uhler. “Causality in the Time of LLMs: Round Table Discussion Results of CLeaR 2023.” In 2nd Conference on Causal Learning and Reasoning, 2023.

Wenzel, Florian, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, et al. “Assaying Out-of-Distribution Generalization in Transfer Learning.” In 36th Conference on Neural Information Processing Systems, 35:7181–98. Neural Information Processing Systems Foundation, 2022.

Lohaus, Michael, Matthäus Kleindessner, Krishnaram Kenthapadi, Francesco Locatello, and Chris Russell. “Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks.” In 36th Conference on Neural Information Processing Systems, 35:16548–62. Neural Information Processing Systems Foundation, 2022.

Dresdner, Gideon, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, and Alp Yurtsever. “Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization.” In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, 151:8439–57. ML Research Press, 2022.

Zietlow, Dominik, Michael Lohaus, Guha Balakrishnan, Matthaus Kleindessner, Francesco Locatello, Bernhard Scholkopf, and Chris Russell. “Leveling down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers.”https://doi.org/10.1109/cvpr52688.2022.01016__In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10400–411. Institute of Electrical and Electronics Engineers, 2022.https://doi.org/10.1109/cvpr52688.2022.01016 .

Rahaman, Nasim, Martin Weiss, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, and Nicolas Ballas. “Neural Attentive Circuits.” In 36th Conference on Neural Information Processing Systems, Vol. 35, 2022.

Dittadi, Andrea, Samuele Papa, Michele De Vita, Bernhard Schölkopf, Ole Winther, and Francesco Locatello. “Generalization and Robustness Implications in Object-Centric Learning.” In Proceedings of the 39th International Conference on Machine Learning, 2022:5221–85. ML Research Press, n.d.

Schott, Lukas, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, and Wieland Brendel. “Visual Representation Learning Does Not Generalize Strongly within the  Same Domain.” In 10th International Conference on Learning Representations, 2022.

Yao, Jian, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David Wipf, Yanwei Fu, and Zheng Zhang. “Self-Supervised Amodal Video Object Segmentation.”https://doi.org/10.48550/arXiv.2210.12733__In 36th Conference on Neural Information Processing Systems, 2022.https://doi.org/10.48550/arXiv.2210.12733 .

Rolland, Paul, Volkan Cevher, Matthäus Kleindessner, Chris Russel, Bernhard Schölkopf, Dominik Janzing, and Francesco Locatello. “Score Matching Enables Causal Discovery of Nonlinear Additive Noise  Models.” In Proceedings of the 39th International Conference on Machine Learning, 162:18741–53. ML Research Press, 2022.

Dittadi, Andrea, Frederik Träuble, Manuel Wüthrich, Felix Widmaier, Peter Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer. “The Role of Pretrained Representations for the OOD Generalization of  Reinforcement Learning Agents.” In 10th International Conference on Learning Representations, 2022.

Makansi, Osama, Julius von Kügelgen, Francesco Locatello, Peter Gehler, Dominik Janzing, Thomas Brox, and Bernhard Schölkopf. “You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction.” In 10th International Conference on Learning Representations, 2022.

Mambelli, Davide, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf, and Francesco Locatello. “Compositional Multi-Object Reinforcement Learning with Linear Relation Networks.”https://doi.org/10.48550/arXiv.2201.13388__ArXiv, n.d.https://doi.org/10.48550/arXiv.2201.13388 .

Rahaman, Nasim, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, and Bernhard Schölkopf. “A General Purpose Neural Architecture for Geospatial Systems.” In 36th Conference on Neural Information Processing Systems, n.d.

Norelli, Antonio, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà, and Francesco Locatello. “ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training.”https://doi.org/10.48550/arXiv.2210.01738__ArXiv, n.d.https://doi.org/10.48550/arXiv.2210.01738 .

Träuble, Frederik, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer. “On Disentangled Representations Learned from Correlated Data.” In Proceedings of the 38th International Conference on Machine Learning, 139:10401–12. ML Research Press, 2021.

Yèche, Hugo, Gideon Dresdner, Francesco Locatello, Matthias Hüser, and Gunnar Rätsch. “Neighborhood Contrastive Learning Applied to Online Patient Monitoring.” In Proceedings of 38th International Conference on Machine Learning, 139:11964–74. ML Research Press, 2021.

Träuble, Frederik, Julius von Kügelgen, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, and Peter Gehler. “Backward-Compatible Prediction Updates: A Probabilistic Approach.” In 35th Conference on Neural Information Processing Systems, 34:116–28, 2021.

Dresdner, Gideon, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, and Gunnar Rätsch. “Boosting Variational Inference with Locally Adaptive Step-Sizes.”https://doi.org/10.24963/ijcai.2021/322__In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2337–43. International Joint Conferences on Artificial Intelligence, 2021.https://doi.org/10.24963/ijcai.2021/322 .

Kügelgen, Julius von, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello. “Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style.” In Advances in Neural Information Processing Systems, 34:16451–67, 2021.

Rahaman, Nasim, Muhammad Waleed Gondal, Shruti Joshi, Peter Gehler, Yoshua Bengio, Francesco Locatello, and Bernhard Schölkopf. “Dynamic Inference with Neural Interpreters.” In Advances in Neural Information Processing Systems, 34:10985–98, 2021.

Scholkopf, Bernhard, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. “Toward Causal Representation Learning.”https://doi.org/10.1109/jproc.2021.3058954__Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2021.https://doi.org/10.1109/jproc.2021.3058954 .

Dittadi, Andrea, Frederik Träuble, Francesco Locatello, Manuel Wüthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, and Bernhard Schölkopf. “On the Transfer of Disentangled Representations in Realistic Settings.” In The Ninth International Conference on Learning Representations, 2021.

Locatello, Francesco. “Enforcing and Discovering Structure in Machine Learning.”https://doi.org/10.48550/arXiv.2111.13693__ArXiv, n.d.https://doi.org/10.48550/arXiv.2111.13693 .

Träuble, Frederik, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer. “Representation Learning for Out-of-Distribution Generalization in Reinforcement Learning.” In ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021.

Stark, Stefan G, Joanna Ficek, Francesco Locatello, Ximena Bonilla, Stéphane Chevrier, Franziska Singer, Rudolf Aebersold, et al. “SCIM: Universal Single-Cell Matching with Unpaired Feature Sets.”https://doi.org/10.1093/bioinformatics/btaa843__Bioinformatics. Oxford University Press, 2020.https://doi.org/10.1093/bioinformatics/btaa843 .

Locatello, Francesco, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem. “A Commentary on the Unsupervised Learning of Disentangled Representations.”https://doi.org/10.1609/aaai.v34i09.7120__In The 34th AAAI Conference on Artificial Intelligence, 34:13681–84. Association for the Advancement of Artificial Intelligence, 2020.https://doi.org/10.1609/aaai.v34i09.7120 .

Locatello, Francesco, Ben Poole, Gunnar Rätsch, Bernhard Schölkopf, Olivier Bachem, and Michael Tschannen. “Weakly-Supervised Disentanglement without Compromises.” In Proceedings of the 37th International Conference on Machine Learning, 119:6348–6359, 2020.

Négiar, Geoffrey, Gideon Dresdner, Alicia Tsai, Laurent El Ghaoui, Francesco Locatello, Robert M. Freund, and Fabian Pedregosa. “Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization.” In Proceedings of the 37th International Conference on Machine Learning, 119:7253–62, 2020.

Locatello, Francesco, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem. “A Sober Look at the Unsupervised Learning of Disentangled Representations and Their Evaluation.” Journal of Machine Learning Research. MIT Press, 2020.

Locatello, Francesco, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. “Object-Centric Learning with Slot Attention.” In Advances in Neural Information Processing Systems, 33:11525–38. Curran Associates, 2020.

Locatello, Francesco, Michael Tschannen, Stefan Bauer, Gunnar Rätsch, Bernhard Schölkopf, and Olivier Bachem. “Disentangling Factors of Variation Using Few Labels.” In 8th International Conference on Learning Representations, 2019.

Gresele, Luigi, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, and Bernhard Schölkopf. “The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA.” In Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence, 115:217–27. ML Research Press, 2019.

Locatello, Francesco, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, and Olivier Bachem. “On the Fairness of Disentangled Representations.” In Advances in Neural Information Processing Systems, 32:14611–14624, 2019.

Locatello, Francesco, Alp Yurtsever, Olivier Fercoq, and Volkan Cevher. “Stochastic Frank-Wolfe for Composite Convex Minimization.” In Advances in Neural Information Processing Systems, 32:14291–14301, 2019.

Steenkiste, Sjoerd van, Francesco Locatello, Jürgen Schmidhuber, and Olivier Bachem. “Are Disentangled Representations Helpful for Abstract Visual Reasoning?” In Advances in Neural Information Processing Systems, Vol. 32, 2019.

Locatello, Francesco, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem. “Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.” In Proceedings of the 36th International Conference on Machine Learning, 97:4114–24. ML Research Press, 2019.

Gondal, Muhammad Waleed, Manuel Wüthrich, ore Miladinovi, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer. “On the Transfer of Inductive Bias from Simulation to the Real World: A New Disentanglement Dataset.” In Advances in Neural Information Processing Systems, Vol. 32, 2019.

Locatello, Francesco, Gideon Dresdner, Rajiv Khanna, Isabel Valera, and Gunnar Rätsch. “Boosting Black Box Variational Inference.” In Advances in Neural Information Processing Systems, Vol. 31. Neural Information Processing Systems Foundation, 2018.

Locatello, Francesco, Rajiv Khanna, Joydeep Ghosh, and Gunnar Rätsch. “Boosting Variational Inference: An Optimization Perspective.” In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, 84:464–72. ML Research Press, 2018.

Fortuin, Vincent, Matthias Hüser, Francesco Locatello, Heiko Strathmann, and Gunnar Rätsch. “SOM-VAE: Interpretable Discrete Representation Learning on Time Series.” In International Conference on Learning Representations, 2018.

Yurtsever, Alp, Olivier Fercoq, Francesco Locatello, and Volkan Cevher. “A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming.” In Proceedings of the 35th International Conference on Machine Learning, 80:5727–36. ML Research Press, 2018.

Locatello, Francesco, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, and Martin Jaggi. “On Matching Pursuit and Coordinate Descent.” In Proceedings of the 35th International Conference on Machine Learning, 80:3198–3207. ML Research Press, 2018.

Locatello, Francesco, Damien Vincent, Ilya Tolstikhin, Gunnar Ratsch, Sylvain Gelly, and Bernhard Scholkopf. “Clustering Meets Implicit Generative Models.” In 6th International Conference on Learning Representations, 2018.

Locatello, Francesco, Damien Vincent, Ilya Tolstikhin, Gunnar Rätsch, Sylvain Gelly, and Bernhard Schölkopf. “Competitive Training of Mixtures of Independent Deep Generative Models.”https://doi.org/10.48550/arXiv.1804.11130__ArXiv, n.d.https://doi.org/10.48550/arXiv.1804.11130 .

Locatello, Francesco, Michael Tschannen, Gunnar Rätsch, and Martin Jaggi. “Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees.” In Advances in Neural Information Processing Systems, 2017.

Locatello, Francesco, Rajiv Khanna, Michael Tschannen, and Martin Jaggi. “A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe.” In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 54:860–68. ML Research Press, 2017.