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71 Publications
2019 | Conference Paper | IST-REx-ID: 14200 | 
Locatello, F., Bauer, S., Lucic, M., Rätsch, G., Gelly, S., Schölkopf, B., & Bachem, O. (2019). Challenging common assumptions in the unsupervised learning of disentangled representations. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 4114–4124). Long Beach, CA, United States: ML Research Press.
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| arXiv
2019 | Conference Paper | IST-REx-ID: 14190 |
Gondal, M. W., Wüthrich, M., Miladinović, Đ., Locatello, F., Breidt, M., Volchkov, V., … Bauer, S. (2019). 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). Vancouver, Canada.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 14202 |
Locatello, F., Dresdner, G., Khanna, R., Valera, I., & Rätsch, G. (2018). Boosting black box variational inference. In Advances in Neural Information Processing Systems (Vol. 31). Montreal, Canada: Neural Information Processing Systems Foundation.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 14201 |
Locatello, F., Khanna, R., Ghosh, J., & Rätsch, G. (2018). Boosting variational inference: An optimization perspective. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (Vol. 84, pp. 464–472). Playa Blanca, Lanzarote: ML Research Press.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 14198 |
Fortuin, V., Hüser, M., Locatello, F., Strathmann, H., & Rätsch, G. (2018). SOM-VAE: Interpretable discrete representation learning on time series. In International Conference on Learning Representations. New Orleans, LA, United States.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 14203 |
Yurtsever, A., Fercoq, O., Locatello, F., & Cevher, V. (2018). A conditional gradient framework for composite convex minimization with applications to semidefinite programming. In Proceedings of the 35th International Conference on Machine Learning (Vol. 80, pp. 5727–5736). Stockholm, Sweden: ML Research Press.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 14204 |
Locatello, F., Raj, A., Karimireddy, S. P., Rätsch, G., Schölkopf, B., Stich, S. U., & Jaggi, M. (2018). On matching pursuit and coordinate descent. In Proceedings of the 35th International Conference on Machine Learning (Vol. 80, pp. 3198–3207). ML Research Press.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 14224 |
Locatello, F., Vincent, D., Tolstikhin, I., Ratsch, G., Gelly, S., & Scholkopf, B. (2018). Clustering meets implicit generative models. In 6th International Conference on Learning Representations. Vancouver, Canada.
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| arXiv
2018 | Preprint | IST-REx-ID: 14327 |
Locatello, F., Vincent, D., Tolstikhin, I., Rätsch, G., Gelly, S., & Schölkopf, B. (n.d.). Competitive training of mixtures of independent deep generative models. arXiv. https://doi.org/10.48550/arXiv.1804.11130
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| arXiv
2017 | Conference Paper | IST-REx-ID: 14206 |
Locatello, F., Tschannen, M., Rätsch, G., & Jaggi, M. (2017). Greedy algorithms for cone constrained optimization with convergence guarantees. In Advances in Neural Information Processing Systems. Long Beach, CA, United States.
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| arXiv
2017 | Conference Paper | IST-REx-ID: 14205 |
Locatello, F., Khanna, R., Tschannen, M., & Jaggi, M. (2017). A unified optimization view on generalized matching pursuit and Frank-Wolfe. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (Vol. 54, pp. 860–868). Fort Lauderdale, FL, United States: ML Research Press.
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| arXiv