Francesco Locatello
Francesco Locatello
PhD student, ETH Zürich, Max Planck Institute for Intelligent Systems
Verified email at ethz.ch
Title
Cited by
Cited by
Year
Challenging common assumptions in the unsupervised learning of disentangled representations
F Locatello, S Bauer, M Lucic, G Rätsch, S Gelly, B Schölkopf, O Bachem
ICML 2019 - Proceedings of the 36th International Conference on Machine …, 2018
1752018
A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe
F Locatello, R Khanna, M Tschannen, M Jaggi
AISTATS 2017 - Proceedings of the 20th International Conference on Artifcial …, 2017
382017
SOM-VAE: Interpretable discrete representation learning on time series
V Fortuin, M Hüser, F Locatello, H Strathmann, G Rätsch
ICLR 2019 - Seventh International Conference on Learning Representations, 2018
222018
On the Fairness of Disentangled Representations
F Locatello, G Abbati, T Rainforth, S Bauer, B Schölkopf, O Bachem
NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, 2019
172019
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees
F Locatello, M Tschannen, G Rätsch, M Jaggi
NIPS 2017 - Advances in Neural Information Processing Systems, 2017
172017
Disentangling factors of variation using few labels
F Locatello, M Tschannen, S Bauer, G Rätsch, B Schölkopf, O Bachem
ICLR 2020 - 8th International Conference on Learning Representations, 2019
162019
On Matching Pursuit and Coordinate Descent
F Locatello, A Raj, SP Reddy, G Rätsch, B Schölkopf, SU Stich, M Jaggi
ICML 2018 - Proceedings of the 35th International Conference on Machine Learning, 2018
16*2018
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
S van Steenkiste, F Locatello, J Schmidhuber, O Bachem
NeurIPS 2019: Thirty-third Conference on Neural Information Processing Systems, 2019
142019
Competitive Training of Mixtures of Independent Deep Generative Models
F Locatello, D Vincent, I Tolstikhin, G Rätsch, S Gelly, B Schölkopf
arXiv preprint arXiv:1804.11130, 2018
14*2018
Boosting Variational Inference: an Optimization Perspective
F Locatello, R Khanna, J Ghosh, G Rätsch
AISTATS 2018 - Proceedings of the 21th International Conference on Artifcial …, 2017
142017
Boosting Black Box Variational Inference
F Locatello, G Dresdner, R Khanna, I Valera, G Rätsch
NeurIPS 2018 - Advances in Neural Information Processing Systems (Spotlight), 2018
132018
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming
A Yurtsever, O Fercoq, F Locatello, V Cevher
ICML 2018 - Proceedings of the 35th International Conference on Machine Learning, 2018
132018
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
MW Gondal, M Wüthrich, Đ Miladinović, F Locatello, M Breidt, V Volchkov, ...
arXiv preprint arXiv:1906.03292, 2019
122019
The incomplete rosetta stone problem: Identifiability results for multi-view nonlinear ica
L Gresele, PK Rubenstein, A Mehrjou, F Locatello, B Schölkopf
UAI 2019 - Conference on Uncertainty in Artificial Intelligence, 2019
32019
Weakly-Supervised Disentanglement Without Compromises
F Locatello, B Poole, G Rätsch, B Schölkopf, O Bachem, M Tschannen
arXiv preprint arXiv:2002.02886, 2020
12020
Stochastic Conditional Gradient Method for Composite Convex Minimization
F Locatello, A Yurtsever, O Fercoq, V Cevher
NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, 2019
1*2019
Object-Centric Learning with Slot Attention
F Locatello, D Weissenborn, T Unterthiner, A Mahendran, G Heigold, ...
arXiv preprint arXiv:2006.15055, 2020
2020
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data
F Träuble, E Creager, N Kilbertus, A Goyal, F Locatello, B Schölkopf, ...
arXiv preprint arXiv:2006.07886, 2020
2020
Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization
G Négiar, G Dresdner, A Tsai, LE Ghaoui, F Locatello, F Pedregosa
arXiv preprint arXiv:2002.11860, 2020
2020
SCIM: Universal Single-Cell Matching with Unpaired Feature Sets
SG Stark, J Ficek, K Lehmann, X Bonilla, F Locatello, G Rätsch, ...
bioRxiv, 2020
2020
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