Bombari, Simone, Shayan Kiyani, and Marco Mondelli. “Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels.” In Proceedings of the 40th International Conference on Machine Learning, 202:2738–76. ML Research Press, 2023.

Súkeník, Peter, Marco Mondelli, and Christoph Lampert. “Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model.” In 37th Annual Conference on Neural Information Processing Systems, n.d.

Wu, Diyuan, Vyacheslav Kungurtsev, and Marco Mondelli. “Mean-Field Analysis for Heavy Ball Methods: Dropout-Stability, Connectivity, and Global Convergence.” In Transactions on Machine Learning Research. ML Research Press, 2023.

Shaw, Thomas, Pascal Buri, Michael McCarthy, Evan Miles, and Francesca Pellicciotti. “Air Temperature and Near-Surface Meteorology Datasets on Three Swiss Glaciers - Extreme 2022 Summer.”https://doi.org/10.5281/ZENODO.8277285__Zenodo, 2023.https://doi.org/10.5281/ZENODO.8277285 .

Knaus, Lisa. “The Metabolism of the Developing Brainf: How Large Neutral Amino Acids Modulate Perinatal Neuronal Excitability and Survival.”https://doi.org/10.15479/at:ista:13107__Institute of Science and Technology Austria, 2023.https://doi.org/10.15479/at:ista:13107 .

Knaus, Lisa, Bernadette Basilico, Daniel Malzl, Maria Gerykova Bujalkova, Mateja Smogavec, Lena A. Schwarz, Sarah Gorkiewicz, et al. “Large Neutral Amino Acid Levels Tune Perinatal Neuronal Excitability and Survival.”https://doi.org/10.1016/j.cell.2023.02.037__Cell. Elsevier, 2023.https://doi.org/10.1016/j.cell.2023.02.037 .

Valentini, Marco, Oliver Sagi, Levon Baghumyan, Thijs de Gijsel, Jason Jung, Stefano Calcaterra, Andrea Ballabio, et al. “Radio Frequency Driven Superconducting Diode and Parity Conserving  Cooper Pair Transport in a Two-Dimensional Germanium Hole Gas.”https://doi.org/10.48550/arXiv.2306.07109__ArXiv, n.d.https://doi.org/10.48550/arXiv.2306.07109 .

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 .

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 .

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.

Fu, Teng, YuHao Liu, Jean Barbier, Marco Mondelli, ShanSuo Liang, and TianQi Hou. “Mismatched Estimation of Non-Symmetric Rank-One Matrices Corrupted by Structured Noise.”https://doi.org/10.1109/isit54713.2023.10206671__In Proceedings of 2023 IEEE International Symposium on Information Theory. IEEE, n.d.https://doi.org/10.1109/isit54713.2023.10206671 .

Marensi, Elena, Gökhan Yalniz, and Björn Hof. “Dynamics and Proliferation of Turbulent Stripes in Plane-Poiseuille and Plane-Couette Flows.”https://doi.org/10.1017/jfm.2023.780__Journal of Fluid Mechanics. Cambridge University Press, 2023.https://doi.org/10.1017/jfm.2023.780 .

Jethwa, Rajesh B, Soumyadip Mondal, Bhargavi Pant, and Stefan Alexander Freunberger. “To DISP or Not? The Farreaching Reaction Mechanisms Underpinning Lithiumair Batteries.”https://doi.org/10.1002/anie.202316476__Angewandte Chemie International Edition. Wiley, 2023.https://doi.org/10.1002/anie.202316476 .