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Francesco Locatello

Adeel Pervez

Marco Fumero

Dario Rancati

Clementine Domine

Francesco Montagna

Riccardo Cadei

Shimeng Huang

Dingling Yao

Berker Demirel

Walter Nelson



87 Publications

2020 | Published | Conference Paper | IST-REx-ID: 14326 | OA
Object-centric learning with slot attention
F. Locatello, D. Weissenborn, T. Unterthiner, A. Mahendran, G. Heigold, J. Uszkoreit, A. Dosovitskiy, T. Kipf, in:, 34th International Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2020, pp. 11525–11538.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2019 | Published | Conference Paper | IST-REx-ID: 14184 | OA
Disentangling factors of variation using few labels
F. Locatello, M. Tschannen, S. Bauer, G. Rätsch, B. Schölkopf, O. Bachem, in:, 8th International Conference on Learning Representations, 2019.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2019 | Published | Conference Paper | IST-REx-ID: 14189 | OA
The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA
L. Gresele, P.K. Rubenstein, A. Mehrjou, F. Locatello, B. Schölkopf, in:, Proceedings of the 35th Conference on Uncertainty in Artificial  Intelligence, ML Research Press, 2019, pp. 217–227.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2019 | Published | Conference Paper | IST-REx-ID: 14190 | OA
On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
M.W. Gondal, M. Wüthrich, Đ. Miladinović, F. Locatello, M. Breidt, V. Volchkov, J. Akpo, O. Bachem, B. Schölkopf, S. Bauer, in:, Advances in Neural Information Processing Systems, 2019.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2019 | Published | Conference Paper | IST-REx-ID: 14191 | OA
Stochastic Frank-Wolfe for composite convex minimization
F. Locatello, A. Yurtsever, O. Fercoq, V. Cevher, in:, Advances in Neural Information Processing Systems, 2019, pp. 14291–14301.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2019 | Published | Conference Paper | IST-REx-ID: 14193 | OA
Are disentangled representations helpful for abstract visual reasoning?
S. van Steenkiste, F. Locatello, J. Schmidhuber, O. Bachem, in:, Advances in Neural Information Processing Systems, 2019.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2019 | Published | Conference Paper | IST-REx-ID: 14197 | OA
On the fairness of disentangled representations
F. Locatello, G. Abbati, T. Rainforth, S. Bauer, B. Schölkopf, O. Bachem, in:, Advances in Neural Information Processing Systems, 2019, pp. 14611–14624.
[Preprint] View | Download Preprint (ext.) | arXiv
 
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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