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.
LibreCat: https://research-explorer.ista.ac.at/record/14200
arXiv: 1811.12359

Gondal, M. W., Wüthrich, M., Miladinovi, ., 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.
LibreCat: https://research-explorer.ista.ac.at/record/14190
arXiv: 1906.03292

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.
LibreCat: https://research-explorer.ista.ac.at/record/14202
arXiv: 1806.02185

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.
LibreCat: https://research-explorer.ista.ac.at/record/14201
arXiv: 1708.01733

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.
LibreCat: https://research-explorer.ista.ac.at/record/14198
arXiv: 1806.02199

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.
LibreCat: https://research-explorer.ista.ac.at/record/14203
arXiv: 1804.08544

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.
LibreCat: https://research-explorer.ista.ac.at/record/14204
arXiv: 1803.09539

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.
LibreCat: https://research-explorer.ista.ac.at/record/14224
arXiv: 1804.11130

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 
LibreCat: https://research-explorer.ista.ac.at/record/14327
DOI: 10.48550/arXiv.1804.11130
arXiv: 1804.11130

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.
LibreCat: https://research-explorer.ista.ac.at/record/14206
arXiv: 1705.11041

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.
LibreCat: https://research-explorer.ista.ac.at/record/14205
arXiv: 1702.06457