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Francesco Locatello

Berker Demirel

Marco Fumero

Dingling Yao

Adeel Pervez



70 Publications

2019 | Published | Conference Paper | IST-REx-ID: 14200 | OA
Challenging common assumptions in the unsupervised learning of disentangled representations
F. Locatello, S. Bauer, M. Lucic, G. Rätsch, S. Gelly, B. Schölkopf, O. Bachem, in:, Proceedings of the 36th International Conference on Machine Learning, ML Research Press, 2019, pp. 4114–4124.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2018 | Published | Conference Paper | IST-REx-ID: 14198 | OA
SOM-VAE: Interpretable discrete representation learning on time series
V. Fortuin, M. Hüser, F. Locatello, H. Strathmann, G. Rätsch, in:, International Conference on Learning Representations, 2018.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2018 | Published | Conference Paper | IST-REx-ID: 14201 | OA
Boosting variational inference: An optimization perspective
F. Locatello, R. Khanna, J. Ghosh, G. Rätsch, in:, Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, ML Research Press, 2018, pp. 464–472.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2018 | Published | Conference Paper | IST-REx-ID: 14202 | OA
Boosting black box variational inference
F. Locatello, G. Dresdner, R. Khanna, I. Valera, G. Rätsch, in:, Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2018.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2018 | Published | Conference Paper | IST-REx-ID: 14203 | OA
A conditional gradient framework for composite convex minimization with applications to semidefinite programming
A. Yurtsever, O. Fercoq, F. Locatello, V. Cevher, in:, Proceedings of the 35th International Conference on Machine Learning, ML Research Press, 2018, pp. 5727–5736.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2018 | Published | Conference Paper | IST-REx-ID: 14204 | OA
On matching pursuit and coordinate descent
F. Locatello, A. Raj, S.P. Karimireddy, G. Rätsch, B. Schölkopf, S.U. Stich, M. Jaggi, in:, Proceedings of the 35th International Conference on Machine Learning, ML Research Press, 2018, pp. 3198–3207.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2018 | Published | Conference Paper | IST-REx-ID: 14224 | OA
Clustering meets implicit generative models
F. Locatello, D. Vincent, I. Tolstikhin, G. Ratsch, S. Gelly, B. Scholkopf, in:, 6th International Conference on Learning Representations, 2018.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2018 | Submitted | Preprint | IST-REx-ID: 14327 | OA
Competitive training of mixtures of independent deep generative models
F. Locatello, D. Vincent, I. Tolstikhin, G. Rätsch, S. Gelly, B. Schölkopf, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
2017 | Published | Conference Paper | IST-REx-ID: 14205 | OA
A unified optimization view on generalized matching pursuit and Frank-Wolfe
F. Locatello, R. Khanna, M. Tschannen, M. Jaggi, in:, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2017, pp. 860–868.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2017 | Published | Conference Paper | IST-REx-ID: 14206 | OA
Greedy algorithms for cone constrained optimization with convergence guarantees
F. Locatello, M. Tschannen, G. Rätsch, M. Jaggi, in:, Advances in Neural Information Processing Systems, 2017.
[Preprint] View | Download Preprint (ext.) | arXiv
 

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