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71 Publications
2018 | Conference Paper | IST-REx-ID: 14203 | 
A. Yurtsever, O. Fercoq, F. Locatello, and V. Cevher, “A conditional gradient framework for composite convex minimization with applications to semidefinite programming,” in Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 2018, vol. 80, pp. 5727–5736.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 14204 |
F. Locatello et al., “On matching pursuit and coordinate descent,” in Proceedings of the 35th International Conference on Machine Learning, 2018, vol. 80, pp. 3198–3207.
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| arXiv
2018 | Conference Paper | IST-REx-ID: 14224 |
F. Locatello, D. Vincent, I. Tolstikhin, G. Ratsch, S. Gelly, and B. Scholkopf, “Clustering meets implicit generative models,” in 6th International Conference on Learning Representations, Vancouver, Canada, 2018.
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| arXiv
2018 | Preprint | IST-REx-ID: 14327 |
F. Locatello, D. Vincent, I. Tolstikhin, G. Rätsch, S. Gelly, and B. Schölkopf, “Competitive training of mixtures of independent deep generative models,” arXiv. .
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| arXiv
2017 | Conference Paper | IST-REx-ID: 14206 |
F. Locatello, M. Tschannen, G. Rätsch, and M. Jaggi, “Greedy algorithms for cone constrained optimization with convergence guarantees,” in Advances in Neural Information Processing Systems, Long Beach, CA, United States, 2017.
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| arXiv
2017 | Conference Paper | IST-REx-ID: 14205 |
F. Locatello, R. Khanna, M. Tschannen, and M. Jaggi, “A unified optimization view on generalized matching pursuit and Frank-Wolfe,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States, 2017, vol. 54, pp. 860–868.
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| arXiv