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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Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., Bachem, O.
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Proceedings of the 36th International Conference on Machine Learning (ICML), 97, pages: 4114-4124, Proceedings of Machine Learning Research, (Editors: Chaudhuri, Kamalika and Salakhutdinov, Ruslan), PMLR, June 2019 (conference)
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Fortuin, V., Hüser, M., Locatello, F., Strathmann, H., Rätsch, G.
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
7th International Conference on Learning Representations (ICLR), May 2019 (conference) Accepted
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Locatello*, F., Dresdner*, G., R., K., Valera, I., Rätsch, G.
Boosting Black Box Variational Inference
Advances in Neural Information Processing Systems 31, pages: 3405-3415, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 32th Annual Conference on Neural Information Processing Systems, December 2018, *equal contribution (conference)
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Yurtsever, A., Fercoq, O., Locatello, F., Cevher, V.
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming
Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 5713-5722, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)
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Locatello, F., Raj, A., Praneeth Karimireddy, S., Rätsch, G., Schölkopf, B., Stich, S. U., Jaggi, M.
On Matching Pursuit and Coordinate Descent
Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 3204-3213, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, July 2018 (conference)
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Locatello, F., Khanna, R., Ghosh, J., Rätsch, G.
Boosting Variational Inference: an Optimization Perspective
Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 84, pages: 464-472, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, April 2018 (conference)
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Locatello, F., Khanna, R., Ghosh, J., Rätsch, G.
Boosting Variational Inference: an Optimization Perspective
Workshop: Advances in Approximate Bayesian Inference at the 31st Conference on Neural Information Processing Systems, December 2017 (conference)
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Locatello, F., Tschannen, M., Rätsch, G., Jaggi, M.
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
Advances in Neural Information Processing Systems 30, pages: 773-784, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., 31st Annual Conference on Neural Information Processing Systems, December 2017 (conference)
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