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Francesco Locatello

Empirical Inference Alumni

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Office: no office Max-Planck-Ring 4 72076 Tübingen +49 7071 601 551 +49 7071 601 552 [email protected]

I graduated cum laude from the University of Padua in Information Engineering and then joined ETH for my master in Computer Science. During this time I became passionate about Machine Learning and I am now part of the Max Planck-ETH Center for Learning Systems, supervised by Gunnar Rätsch and Bernhard Schölkopf. I am very passionate about optimization for machine learning, causality, and unsupervised learning. Recently I've been working on improving approximate Bayesian inference and representation learning. I'm currently working part-time in Google AI as a research consultant in collaboration with ETH (MSRA). My research lays at the intersection of convex optimization, Bayesian inference, and representation learning.

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Autonomous Learning Empirical Inference Conference Paper Bridging the Gap to Real-World Object-Centric Learning Seitzer, M., Horn, M., Zadaianchuk, A., Zietlow, D., Xiao, T., Simon-Gabriel, C., He, T., Zhang, Z., Schölkopf, B., Brox, T., et al. In Proceedings of the Eleventh International Conference on Learning Representations, The Eleventh International Conference on Learning Representations (ICLR), May 2023 (Published) Code Website URL BibTeX
Empirical Inference Conference Paper The Role of Pretrained Representations for the OOD Generalization of RL Agents Träuble*, F., Dittadi*, A., Wüthrich, M., Widmaier, F., Gehler, P., Winther, O., Locatello, F., Bachem, O., Schölkopf, B., Bauer, S. The Tenth International Conference on Learning Representations (ICLR 2022), International Conference on Learning Representations (ICLR 2022), April 2022, *equal contribution (Published) URL BibTeX
Empirical Inference Conference Paper Visual Representation Learning Does Not Generalize Strongly Within the Same Domain Schott, L., von Kügelgen, J., Träuble, F., Gehler, P., Russell, C., Bethge, M., Schölkopf, B., Locatello, F., Brendel, W. The Tenth International Conference on Learning Representations (ICLR 2022), 10th International Conference on Learning Representations (ICLR), April 2022 (Published) URL BibTeX
Empirical Inference Conference Paper You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction Makansi, O., von Kügelgen, J., Locatello, F., Gehler, P., Janzing, D., Brox, T., Schölkopf, B. 10th International Conference on Learning Representations (ICLR), April 2022 (Published) arXiv URL BibTeX
Empirical Inference Conference Paper Backward-Compatible Prediction Updates: A Probabilistic Approach Träuble, F., von Kügelgen, J., Kleindessner, M., Locatello, F., Schölkopf, B., Gehler, P. Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 116-128, (Editors: M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan), Curran Associates, Inc., 35th Annual Conference on Neural Information Processing Systems (NeurIPS), December 2021 (Published) arXiv URL BibTeX
Empirical Inference Conference Paper Dynamic Inference with Neural Interpreters Rahaman*, N., Gondal*, M. W., Joshi, S., Gehler, P., Bengio, Y., Locatello, F., Schölkopf, B. Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 10985-10998, (Editors: M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan), Curran Associates, Inc., 35th Annual Conference on Neural Information Processing Systems, December 2021, *equal contribution (Published) URL BibTeX
Empirical Inference Conference Paper Self-supervised learning with data augmentations provably isolates content from style von Kügelgen*, J., Sharma*, Y., Gresele*, L., Brendel, W., Schölkopf, B., Besserve, M., Locatello, F. Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 16451-16467, (Editors: M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan), Curran Associates, Inc., 35th Annual Conference on Neural Information Processing Systems, December 2021, *equal contribution (Published) arXiv URL BibTeX
Empirical Inference Conference Paper Boosting Variational Inference With Locally Adaptive Step-Sizes Dresdner, G., Shekhar, S., Pedregosa, F., Locatello, F., Rätsch, G. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2337-2343, (Editors: Zhi-Hua Zhou), August 2021 (Published) arXiv DOI BibTeX
Empirical Inference Conference Paper Neighborhood Contrastive Learning Applied to Online Patient Monitoring Yèche, H., Dresdner, G., Locatello, F., Hüser, M., Rätsch, G. Proceedings of 38th International Conference on Machine Learning, 139:11964-11974, Proceedings of Machine Learning Research, (Editors: Meila, Marina and Zhang, Tong), PMLR, The 38th International Conference on Machine Learning (ICML 2021), July 2021 (Published) URL BibTeX
Empirical Inference Conference Paper On Disentangled Representations Learned From Correlated Data Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal, A., Schölkopf, B., Bauer, S. Proceedings of 38th International Conference on Machine Learning (ICML), 139:10401-10412, Proceedings of Machine Learning Research, (Editors: Meila, Marina and Zhang, Tong), PMLR, July 2021 (Published) URL BibTeX
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