Francesco Locatello

Locatello Group

70 Publications

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[70]
2024 | Published | Conference Paper | IST-REx-ID: 14213 | OA
Divided attention: Unsupervised multi-object discovery with contextually separated slots
Lao, Dong, Divided attention: Unsupervised multi-object discovery with contextually separated slots. 1st Conference on Parsimony and Learning. 2024
[Published Version] View | Files available | arXiv
 
[69]
2023 | Published | Conference Paper | IST-REx-ID: 14105 | OA
TeST: Test-time Self-Training under distribution shift
S. Sinha, P. Gehler, F. Locatello, B. Schiele, in:, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2023.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[68]
2023 | Submitted | Preprint | IST-REx-ID: 14207 | OA
Rotating features for object discovery
S. Löwe, P. Lippe, F. Locatello, M. Welling, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[67]
2023 | Published | Conference Paper | IST-REx-ID: 14208 | OA
Benign overfitting in deep neural networks under lazy training
Z. Zhu, F. Liu, G.G. Chrysos, F. Locatello, V. Cevher, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 43105–43128.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[66]
2023 | Submitted | Preprint | IST-REx-ID: 14209 | OA
A data augmentation perspective on diffusion models and retrieval
M.F. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[65]
2023 | Submitted | Preprint | IST-REx-ID: 14210 | OA
Leveraging sparse and shared feature activations for disentangled representation learning
M. Fumero, F. Wenzel, L. Zancato, A. Achille, E. Rodolà, S. Soatto, B. Schölkopf, F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[64]
2023 | Published | Conference Paper | IST-REx-ID: 14211 | OA
Causal discovery with score matching on additive models with arbitrary noise
F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[63]
2023 | Published | Conference Paper | IST-REx-ID: 14212 | OA
Scalable causal discovery with score matching
F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[62]
2023 | Published | Conference Paper | IST-REx-ID: 14214 | OA
Causal triplet: An open challenge for intervention-centric causal representation learning
Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[61]
2023 | Published | Conference Paper | IST-REx-ID: 14217 | OA
Relative representations enable zero-shot latent space communication
L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, E. Rodolà, in:, The 11th International Conference on Learning Representations, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[60]
2023 | Published | Conference Paper | IST-REx-ID: 14218 | OA
Bridging the gap to real-world object-centric learning
M. Seitzer, M. Horn, A. Zadaianchuk, D. Zietlow, T. Xiao, C.-J.S.-G. Carl-Johann Simon-Gabriel, T. He, Z. Zhang, B. Schölkopf, T. Brox, F. Locatello, in:, The 11th International Conference on Learning Representations, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[59]
2023 | Published | Conference Paper | IST-REx-ID: 14219 | OA
Unsupervised semantic segmentation with self-supervised object-centric representations
A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, T. Brox, in:, The 11th International Conference on Learning Representations, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[58]
2023 | Published | Conference Paper | IST-REx-ID: 14222 | OA
Unsupervised object learning via common fate
M. Tangemann, S. Schneider, J. von Kügelgen, F. Locatello, P. Gehler, T. Brox, M. Kümmerer, M. Bethge, B. Schölkopf, in:, 2nd Conference on Causal Learning and Reasoning, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[57]
2023 | Submitted | Preprint | IST-REx-ID: 14333 | OA
Self-compatibility: Evaluating causal discovery without ground truth
P.M. Faller, L.C. Vankadara, A.A. Mastakouri, F. Locatello, D. Janzing, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[56]
2023 | Submitted | Preprint | IST-REx-ID: 14946 | OA
Multi-view causal representation learning with partial observability
D. Yao, D. Xu, S. Lachapelle, S. Magliacane, P. Taslakian, G. Martius, J. von Kügelgen, F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[55]
2023 | Submitted | Preprint | IST-REx-ID: 14948 | OA
Grounded object centric learning
A. Kori, F. Locatello, F.D.S. Ribeiro, F. Toni, B. Glocker, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[54]
2023 | Published | Journal Article | IST-REx-ID: 14949 | OA
Image retrieval outperforms diffusion models on data augmentation
M. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, Journal of Machine Learning Research (2023).
[Published Version] View | Files available | Download Published Version (ext.)
 
[53]
2023 | Submitted | Preprint | IST-REx-ID: 14952 | OA
Latent space translation via semantic alignment
V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, E. Rodolà, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[52]
2023 | Submitted | Preprint | IST-REx-ID: 14953 | OA
Sample complexity bounds for score-matching: Causal discovery and generative modeling
Z. Zhu, F. Locatello, V. Cevher, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[51]
2023 | Submitted | Preprint | IST-REx-ID: 14954 | OA
Assumption violations in causal discovery and the robustness of score matching
F. Montagna, A.A. Mastakouri, E. Eulig, N. Noceti, L. Rosasco, D. Janzing, B. Aragam, F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[50]
2023 | Published | Conference Paper | IST-REx-ID: 14958 | OA
A sparsity principle for partially observable causal representation learning
D. Xu, D. Yao, S. Lachapelle, P. Taslakian, J. von Kügelgen, F. Locatello, S. Magliacane, in:, Causal Representation Learning Workshop at NeurIPS 2023, OpenReview, 2023.
[Published Version] View | Files available | Download Published Version (ext.)
 
[49]
2023 | Submitted | Preprint | IST-REx-ID: 14961 | OA
Shortcuts for causal discovery of nonlinear models by score matching
F. Montagna, N. Noceti, L. Rosasco, F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[48]
2023 | Submitted | Preprint | IST-REx-ID: 14962 | OA
Unsupervised open-vocabulary object localization in videos
K. Fan, Z. Bai, T. Xiao, D. Zietlow, M. Horn, Z. Zhao, C.-J.S.-G. Carl-Johann Simon-Gabriel, M.Z. Shou, F. Locatello, B. Schiele, T. Brox, Z. Zhang, Y. Fu, T. He, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[47]
2023 | Submitted | Preprint | IST-REx-ID: 14963 | OA
Object-centric multiple object tracking
Z. Zhao, J. Wang, M. Horn, Y. Ding, T. He, Z. Bai, D. Zietlow, C.-J.S.-G. Carl-Johann Simon-Gabriel, B. Shuai, Z. Tu, T. Brox, B. Schiele, Y. Fu, F. Locatello, Z. Zhang, T. Xiao, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[46]
2022 | Published | Conference Paper | IST-REx-ID: 14093 | OA
Faster one-sample stochastic conditional gradient method for composite convex minimization
G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, A. Yurtsever, in:, Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2022, pp. 8439–8457.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[45]
2022 | Published | Conference Paper | IST-REx-ID: 14106 | OA
Are two heads the same as one? Identifying disparate treatment in fair neural networks
M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, C. Russell, in:, 36th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2022, pp. 16548–16562.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[44]
2022 | Published | Conference Paper | IST-REx-ID: 14107 | OA
Self-supervised amodal video object segmentation
J. Yao, Y. Hong, C. Wang, T. Xiao, T. He, F. Locatello, D. Wipf, Y. Fu, Z. Zhang, in:, 36th Conference on Neural Information Processing Systems, 2022.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[43]
2022 | Published | Conference Paper | IST-REx-ID: 14114 | OA
Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers
D. Zietlow, M. Lohaus, G. Balakrishnan, M. Kleindessner, F. Locatello, B. Scholkopf, C. Russell, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–10411.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[42]
2022 | Published | Conference Paper | IST-REx-ID: 14168 | OA
Neural attentive circuits
N. Rahaman, M. Weiss, F. Locatello, C. Pal, Y. Bengio, B. Schölkopf, L.E. Li, N. Ballas, in:, 36th Conference on Neural Information Processing Systems, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[41]
2022 | Submitted | Conference Paper | IST-REx-ID: 14170 | OA
Generalization and robustness implications in object-centric learning
A. Dittadi, S. Papa, M.D. Vita, B. Schölkopf, O. Winther, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, n.d., pp. 5221–5285.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[40]
2022 | Published | Conference Paper | IST-REx-ID: 14171 | OA
Score matching enables causal discovery of nonlinear additive noise models
P. Rolland, V. Cevher, M. Kleindessner, C. Russel, B. Schölkopf, D. Janzing, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022, pp. 18741–18753.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[39]
2022 | Published | Conference Paper | IST-REx-ID: 14172 | OA
Visual representation learning does not generalize strongly within the same domain
L. Schott, J. von Kügelgen, F. Träuble, P. Gehler, C. Russell, M. Bethge, B. Schölkopf, F. Locatello, W. Brendel, in:, 10th International Conference on Learning Representations, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[38]
2022 | Published | Conference Paper | IST-REx-ID: 14173 | OA
Assaying out-of-distribution generalization in transfer learning
F. Wenzel, A. Dittadi, P.V. Gehler, C.-J.S.-G. Carl-Johann Simon-Gabriel, M. Horn, D. Zietlow, D. Kernert, C. Russell, T. Brox, B. Schiele, B. Schölkopf, F. Locatello, in:, 36th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2022, pp. 7181–7198.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[37]
2022 | Published | Conference Paper | IST-REx-ID: 14174 | OA
The role of pretrained representations for the OOD generalization of reinforcement learning agents
A. Dittadi, F. Träuble, M. Wüthrich, F. Widmaier, P. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, 10th International Conference on Learning Representations, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[36]
2022 | Published | Conference Paper | IST-REx-ID: 14175 | OA
You mostly walk alone: Analyzing feature attribution in trajectory prediction
O. Makansi, J. von Kügelgen, F. Locatello, P. Gehler, D. Janzing, T. Brox, B. Schölkopf, in:, 10th International Conference on Learning Representations, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[35]
2022 | Submitted | Conference Paper | IST-REx-ID: 14215 | OA
A general purpose neural architecture for geospatial systems
N. Rahaman, M. Weiss, F. Träuble, F. Locatello, A. Lacoste, Y. Bengio, C. Pal, L.E. Li, B. Schölkopf, in:, 36th Conference on Neural Information Processing Systems, n.d.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[34]
2022 | Submitted | Preprint | IST-REx-ID: 14216 | OA
ASIF: Coupled data turns unimodal models to multimodal without training
A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[33]
2022 | Submitted | Preprint | IST-REx-ID: 14220 | OA
Compositional multi-object reinforcement learning with linear relation networks
D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[32]
2021 | Published | Journal Article | IST-REx-ID: 14117 | OA
Toward causal representation learning
B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
[31]
2021 | Published | Conference Paper | IST-REx-ID: 14176 | OA
Neighborhood contrastive learning applied to online patient monitoring
H. Yèche, G. Dresdner, F. Locatello, M. Hüser, G. Rätsch, in:, Proceedings of 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 11964–11974.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[30]
2021 | Published | Conference Paper | IST-REx-ID: 14177 | OA
On disentangled representations learned from correlated data
F. Träuble, E. Creager, N. Kilbertus, F. Locatello, A. Dittadi, A. Goyal, B. Schölkopf, S. Bauer, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 10401–10412.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[29]
2021 | Published | Conference Paper | IST-REx-ID: 14178 | OA
On the transfer of disentangled representations in realistic settings
A. Dittadi, F. Träuble, F. Locatello, M. Wüthrich, V. Agrawal, O. Winther, S. Bauer, B. Schölkopf, in:, The Ninth International Conference on Learning Representations, 2021.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[28]
2021 | Published | Conference Paper | IST-REx-ID: 14179 | OA
Self-supervised learning with data augmentations provably isolates content from style
J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, F. Locatello, in:, Advances in Neural Information Processing Systems, 2021, pp. 16451–16467.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[27]
2021 | Published | Conference Paper | IST-REx-ID: 14180 | OA
Dynamic inference with neural interpreters
N. Rahaman, M.W. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, B. Schölkopf, in:, Advances in Neural Information Processing Systems, 2021, pp. 10985–10998.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[26]
2021 | Published | Conference Paper | IST-REx-ID: 14181 | OA
Boosting variational inference with locally adaptive step-sizes
G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, G. Rätsch, in:, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–2343.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
[25]
2021 | Published | Conference Paper | IST-REx-ID: 14182 | OA
Backward-compatible prediction updates: A probabilistic approach
F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, P. Gehler, in:, 35th Conference on Neural Information Processing Systems, 2021, pp. 116–128.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[24]
2021 | Submitted | Preprint | IST-REx-ID: 14221 | OA
Enforcing and discovering structure in machine learning
F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[23]
2021 | Published | Conference Paper | IST-REx-ID: 14332
Representation learning for out-of-distribution generalization in reinforcement learning
F. Träuble, A. Dittadi, M. Wuthrich, F. Widmaier, P.V. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021.
View
 
[22]
2020 | Published | Journal Article | IST-REx-ID: 14125 | OA
SCIM: Universal single-cell matching with unpaired feature sets
Stark SG et al. 2020. SCIM: Universal single-cell matching with unpaired feature sets. Bioinformatics. 36(Supplement_2), i919–i927.
[Published Version] View | Files available | DOI | Download Published Version (ext.) | PubMed | Europe PMC
 
[21]
2020 | Published | Conference Paper | IST-REx-ID: 14186 | OA
A commentary on the unsupervised learning of disentangled representations
F. Locatello, S. Bauer, M. Lucic, G. Rätsch, S. Gelly, B. Schölkopf, O. Bachem, in:, The 34th AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, 2020, pp. 13681–13684.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[20]
2020 | Published | Conference Paper | IST-REx-ID: 14187 | OA
Stochastic Frank-Wolfe for constrained finite-sum minimization
G. Négiar, G. Dresdner, A. Tsai, L.E. Ghaoui, F. Locatello, R.M. Freund, F. Pedregosa, in:, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 7253–7262.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[19]
2020 | Published | Conference Paper | IST-REx-ID: 14188 | OA
Weakly-supervised disentanglement without compromises
F. Locatello, B. Poole, G. Rätsch, B. Schölkopf, O. Bachem, M. Tschannen, in:, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 6348–6359.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[18]
2020 | Published | Journal Article | IST-REx-ID: 14195 | OA
A sober look at the unsupervised learning of disentangled representations and their evaluation
F. Locatello, S. Bauer, M. Lucic, G. Rätsch, S. Gelly, B. Schölkopf, O. Bachem, Journal of Machine Learning Research 21 (2020).
[Published Version] View | Download Published Version (ext.) | arXiv
 
[17]
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:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 11525–11538.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[16]
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
 
[15]
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
 
[14]
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
 
[13]
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
 
[12]
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
 
[11]
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
 
[10]
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
 
[9]
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
 
[8]
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
 
[7]
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
 
[6]
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
 
[5]
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
 
[4]
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
 
[3]
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
 
[2]
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
 
[1]
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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70 Publications

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[70]
2024 | Published | Conference Paper | IST-REx-ID: 14213 | OA
Divided attention: Unsupervised multi-object discovery with contextually separated slots
Lao, Dong, Divided attention: Unsupervised multi-object discovery with contextually separated slots. 1st Conference on Parsimony and Learning. 2024
[Published Version] View | Files available | arXiv
 
[69]
2023 | Published | Conference Paper | IST-REx-ID: 14105 | OA
TeST: Test-time Self-Training under distribution shift
S. Sinha, P. Gehler, F. Locatello, B. Schiele, in:, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Institute of Electrical and Electronics Engineers, 2023.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[68]
2023 | Submitted | Preprint | IST-REx-ID: 14207 | OA
Rotating features for object discovery
S. Löwe, P. Lippe, F. Locatello, M. Welling, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[67]
2023 | Published | Conference Paper | IST-REx-ID: 14208 | OA
Benign overfitting in deep neural networks under lazy training
Z. Zhu, F. Liu, G.G. Chrysos, F. Locatello, V. Cevher, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 43105–43128.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[66]
2023 | Submitted | Preprint | IST-REx-ID: 14209 | OA
A data augmentation perspective on diffusion models and retrieval
M.F. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[65]
2023 | Submitted | Preprint | IST-REx-ID: 14210 | OA
Leveraging sparse and shared feature activations for disentangled representation learning
M. Fumero, F. Wenzel, L. Zancato, A. Achille, E. Rodolà, S. Soatto, B. Schölkopf, F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[64]
2023 | Published | Conference Paper | IST-REx-ID: 14211 | OA
Causal discovery with score matching on additive models with arbitrary noise
F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[63]
2023 | Published | Conference Paper | IST-REx-ID: 14212 | OA
Scalable causal discovery with score matching
F. Montagna, N. Noceti, L. Rosasco, K. Zhang, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[62]
2023 | Published | Conference Paper | IST-REx-ID: 14214 | OA
Causal triplet: An open challenge for intervention-centric causal representation learning
Y. Liu, A. Alahi, C. Russell, M. Horn, D. Zietlow, B. Schölkopf, F. Locatello, in:, 2nd Conference on Causal Learning and Reasoning, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[61]
2023 | Published | Conference Paper | IST-REx-ID: 14217 | OA
Relative representations enable zero-shot latent space communication
L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, E. Rodolà, in:, The 11th International Conference on Learning Representations, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[60]
2023 | Published | Conference Paper | IST-REx-ID: 14218 | OA
Bridging the gap to real-world object-centric learning
M. Seitzer, M. Horn, A. Zadaianchuk, D. Zietlow, T. Xiao, C.-J.S.-G. Carl-Johann Simon-Gabriel, T. He, Z. Zhang, B. Schölkopf, T. Brox, F. Locatello, in:, The 11th International Conference on Learning Representations, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[59]
2023 | Published | Conference Paper | IST-REx-ID: 14219 | OA
Unsupervised semantic segmentation with self-supervised object-centric representations
A. Zadaianchuk, M. Kleindessner, Y. Zhu, F. Locatello, T. Brox, in:, The 11th International Conference on Learning Representations, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[58]
2023 | Published | Conference Paper | IST-REx-ID: 14222 | OA
Unsupervised object learning via common fate
M. Tangemann, S. Schneider, J. von Kügelgen, F. Locatello, P. Gehler, T. Brox, M. Kümmerer, M. Bethge, B. Schölkopf, in:, 2nd Conference on Causal Learning and Reasoning, 2023.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[57]
2023 | Submitted | Preprint | IST-REx-ID: 14333 | OA
Self-compatibility: Evaluating causal discovery without ground truth
P.M. Faller, L.C. Vankadara, A.A. Mastakouri, F. Locatello, D. Janzing, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[56]
2023 | Submitted | Preprint | IST-REx-ID: 14946 | OA
Multi-view causal representation learning with partial observability
D. Yao, D. Xu, S. Lachapelle, S. Magliacane, P. Taslakian, G. Martius, J. von Kügelgen, F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[55]
2023 | Submitted | Preprint | IST-REx-ID: 14948 | OA
Grounded object centric learning
A. Kori, F. Locatello, F.D.S. Ribeiro, F. Toni, B. Glocker, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[54]
2023 | Published | Journal Article | IST-REx-ID: 14949 | OA
Image retrieval outperforms diffusion models on data augmentation
M. Burg, F. Wenzel, D. Zietlow, M. Horn, O. Makansi, F. Locatello, C. Russell, Journal of Machine Learning Research (2023).
[Published Version] View | Files available | Download Published Version (ext.)
 
[53]
2023 | Submitted | Preprint | IST-REx-ID: 14952 | OA
Latent space translation via semantic alignment
V. Maiorca, L. Moschella, A. Norelli, M. Fumero, F. Locatello, E. Rodolà, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[52]
2023 | Submitted | Preprint | IST-REx-ID: 14953 | OA
Sample complexity bounds for score-matching: Causal discovery and generative modeling
Z. Zhu, F. Locatello, V. Cevher, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[51]
2023 | Submitted | Preprint | IST-REx-ID: 14954 | OA
Assumption violations in causal discovery and the robustness of score matching
F. Montagna, A.A. Mastakouri, E. Eulig, N. Noceti, L. Rosasco, D. Janzing, B. Aragam, F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[50]
2023 | Published | Conference Paper | IST-REx-ID: 14958 | OA
A sparsity principle for partially observable causal representation learning
D. Xu, D. Yao, S. Lachapelle, P. Taslakian, J. von Kügelgen, F. Locatello, S. Magliacane, in:, Causal Representation Learning Workshop at NeurIPS 2023, OpenReview, 2023.
[Published Version] View | Files available | Download Published Version (ext.)
 
[49]
2023 | Submitted | Preprint | IST-REx-ID: 14961 | OA
Shortcuts for causal discovery of nonlinear models by score matching
F. Montagna, N. Noceti, L. Rosasco, F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[48]
2023 | Submitted | Preprint | IST-REx-ID: 14962 | OA
Unsupervised open-vocabulary object localization in videos
K. Fan, Z. Bai, T. Xiao, D. Zietlow, M. Horn, Z. Zhao, C.-J.S.-G. Carl-Johann Simon-Gabriel, M.Z. Shou, F. Locatello, B. Schiele, T. Brox, Z. Zhang, Y. Fu, T. He, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[47]
2023 | Submitted | Preprint | IST-REx-ID: 14963 | OA
Object-centric multiple object tracking
Z. Zhao, J. Wang, M. Horn, Y. Ding, T. He, Z. Bai, D. Zietlow, C.-J.S.-G. Carl-Johann Simon-Gabriel, B. Shuai, Z. Tu, T. Brox, B. Schiele, Y. Fu, F. Locatello, Z. Zhang, T. Xiao, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[46]
2022 | Published | Conference Paper | IST-REx-ID: 14093 | OA
Faster one-sample stochastic conditional gradient method for composite convex minimization
G. Dresdner, M.-L. Vladarean, G. Rätsch, F. Locatello, V. Cevher, A. Yurtsever, in:, Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2022, pp. 8439–8457.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[45]
2022 | Published | Conference Paper | IST-REx-ID: 14106 | OA
Are two heads the same as one? Identifying disparate treatment in fair neural networks
M. Lohaus, M. Kleindessner, K. Kenthapadi, F. Locatello, C. Russell, in:, 36th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2022, pp. 16548–16562.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[44]
2022 | Published | Conference Paper | IST-REx-ID: 14107 | OA
Self-supervised amodal video object segmentation
J. Yao, Y. Hong, C. Wang, T. Xiao, T. He, F. Locatello, D. Wipf, Y. Fu, Z. Zhang, in:, 36th Conference on Neural Information Processing Systems, 2022.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[43]
2022 | Published | Conference Paper | IST-REx-ID: 14114 | OA
Leveling down in computer vision: Pareto inefficiencies in fair deep classifiers
D. Zietlow, M. Lohaus, G. Balakrishnan, M. Kleindessner, F. Locatello, B. Scholkopf, C. Russell, in:, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Institute of Electrical and Electronics Engineers, 2022, pp. 10400–10411.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[42]
2022 | Published | Conference Paper | IST-REx-ID: 14168 | OA
Neural attentive circuits
N. Rahaman, M. Weiss, F. Locatello, C. Pal, Y. Bengio, B. Schölkopf, L.E. Li, N. Ballas, in:, 36th Conference on Neural Information Processing Systems, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[41]
2022 | Submitted | Conference Paper | IST-REx-ID: 14170 | OA
Generalization and robustness implications in object-centric learning
A. Dittadi, S. Papa, M.D. Vita, B. Schölkopf, O. Winther, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, n.d., pp. 5221–5285.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[40]
2022 | Published | Conference Paper | IST-REx-ID: 14171 | OA
Score matching enables causal discovery of nonlinear additive noise models
P. Rolland, V. Cevher, M. Kleindessner, C. Russel, B. Schölkopf, D. Janzing, F. Locatello, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022, pp. 18741–18753.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[39]
2022 | Published | Conference Paper | IST-REx-ID: 14172 | OA
Visual representation learning does not generalize strongly within the same domain
L. Schott, J. von Kügelgen, F. Träuble, P. Gehler, C. Russell, M. Bethge, B. Schölkopf, F. Locatello, W. Brendel, in:, 10th International Conference on Learning Representations, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[38]
2022 | Published | Conference Paper | IST-REx-ID: 14173 | OA
Assaying out-of-distribution generalization in transfer learning
F. Wenzel, A. Dittadi, P.V. Gehler, C.-J.S.-G. Carl-Johann Simon-Gabriel, M. Horn, D. Zietlow, D. Kernert, C. Russell, T. Brox, B. Schiele, B. Schölkopf, F. Locatello, in:, 36th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2022, pp. 7181–7198.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[37]
2022 | Published | Conference Paper | IST-REx-ID: 14174 | OA
The role of pretrained representations for the OOD generalization of reinforcement learning agents
A. Dittadi, F. Träuble, M. Wüthrich, F. Widmaier, P. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, 10th International Conference on Learning Representations, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[36]
2022 | Published | Conference Paper | IST-REx-ID: 14175 | OA
You mostly walk alone: Analyzing feature attribution in trajectory prediction
O. Makansi, J. von Kügelgen, F. Locatello, P. Gehler, D. Janzing, T. Brox, B. Schölkopf, in:, 10th International Conference on Learning Representations, 2022.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[35]
2022 | Submitted | Conference Paper | IST-REx-ID: 14215 | OA
A general purpose neural architecture for geospatial systems
N. Rahaman, M. Weiss, F. Träuble, F. Locatello, A. Lacoste, Y. Bengio, C. Pal, L.E. Li, B. Schölkopf, in:, 36th Conference on Neural Information Processing Systems, n.d.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[34]
2022 | Submitted | Preprint | IST-REx-ID: 14216 | OA
ASIF: Coupled data turns unimodal models to multimodal without training
A. Norelli, M. Fumero, V. Maiorca, L. Moschella, E. Rodolà, F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[33]
2022 | Submitted | Preprint | IST-REx-ID: 14220 | OA
Compositional multi-object reinforcement learning with linear relation networks
D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[32]
2021 | Published | Journal Article | IST-REx-ID: 14117 | OA
Toward causal representation learning
B. Scholkopf, F. Locatello, S. Bauer, N.R. Ke, N. Kalchbrenner, A. Goyal, Y. Bengio, Proceedings of the IEEE 109 (2021) 612–634.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
[31]
2021 | Published | Conference Paper | IST-REx-ID: 14176 | OA
Neighborhood contrastive learning applied to online patient monitoring
H. Yèche, G. Dresdner, F. Locatello, M. Hüser, G. Rätsch, in:, Proceedings of 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 11964–11974.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[30]
2021 | Published | Conference Paper | IST-REx-ID: 14177 | OA
On disentangled representations learned from correlated data
F. Träuble, E. Creager, N. Kilbertus, F. Locatello, A. Dittadi, A. Goyal, B. Schölkopf, S. Bauer, in:, Proceedings of the 38th International Conference on Machine Learning, ML Research Press, 2021, pp. 10401–10412.
[Published Version] View | Download Published Version (ext.) | arXiv
 
[29]
2021 | Published | Conference Paper | IST-REx-ID: 14178 | OA
On the transfer of disentangled representations in realistic settings
A. Dittadi, F. Träuble, F. Locatello, M. Wüthrich, V. Agrawal, O. Winther, S. Bauer, B. Schölkopf, in:, The Ninth International Conference on Learning Representations, 2021.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[28]
2021 | Published | Conference Paper | IST-REx-ID: 14179 | OA
Self-supervised learning with data augmentations provably isolates content from style
J. von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, F. Locatello, in:, Advances in Neural Information Processing Systems, 2021, pp. 16451–16467.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[27]
2021 | Published | Conference Paper | IST-REx-ID: 14180 | OA
Dynamic inference with neural interpreters
N. Rahaman, M.W. Gondal, S. Joshi, P. Gehler, Y. Bengio, F. Locatello, B. Schölkopf, in:, Advances in Neural Information Processing Systems, 2021, pp. 10985–10998.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[26]
2021 | Published | Conference Paper | IST-REx-ID: 14181 | OA
Boosting variational inference with locally adaptive step-sizes
G. Dresdner, S. Shekhar, F. Pedregosa, F. Locatello, G. Rätsch, in:, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 2337–2343.
[Published Version] View | DOI | Download Published Version (ext.) | arXiv
 
[25]
2021 | Published | Conference Paper | IST-REx-ID: 14182 | OA
Backward-compatible prediction updates: A probabilistic approach
F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, P. Gehler, in:, 35th Conference on Neural Information Processing Systems, 2021, pp. 116–128.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[24]
2021 | Submitted | Preprint | IST-REx-ID: 14221 | OA
Enforcing and discovering structure in machine learning
F. Locatello, ArXiv (n.d.).
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[23]
2021 | Published | Conference Paper | IST-REx-ID: 14332
Representation learning for out-of-distribution generalization in reinforcement learning
F. Träuble, A. Dittadi, M. Wuthrich, F. Widmaier, P.V. Gehler, O. Winther, F. Locatello, O. Bachem, B. Schölkopf, S. Bauer, in:, ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021.
View
 
[22]
2020 | Published | Journal Article | IST-REx-ID: 14125 | OA
SCIM: Universal single-cell matching with unpaired feature sets
Stark SG et al. 2020. SCIM: Universal single-cell matching with unpaired feature sets. Bioinformatics. 36(Supplement_2), i919–i927.
[Published Version] View | Files available | DOI | Download Published Version (ext.) | PubMed | Europe PMC
 
[21]
2020 | Published | Conference Paper | IST-REx-ID: 14186 | OA
A commentary on the unsupervised learning of disentangled representations
F. Locatello, S. Bauer, M. Lucic, G. Rätsch, S. Gelly, B. Schölkopf, O. Bachem, in:, The 34th AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, 2020, pp. 13681–13684.
[Preprint] View | DOI | Download Preprint (ext.) | arXiv
 
[20]
2020 | Published | Conference Paper | IST-REx-ID: 14187 | OA
Stochastic Frank-Wolfe for constrained finite-sum minimization
G. Négiar, G. Dresdner, A. Tsai, L.E. Ghaoui, F. Locatello, R.M. Freund, F. Pedregosa, in:, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 7253–7262.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[19]
2020 | Published | Conference Paper | IST-REx-ID: 14188 | OA
Weakly-supervised disentanglement without compromises
F. Locatello, B. Poole, G. Rätsch, B. Schölkopf, O. Bachem, M. Tschannen, in:, Proceedings of the 37th International Conference on Machine Learning, 2020, pp. 6348–6359.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[18]
2020 | Published | Journal Article | IST-REx-ID: 14195 | OA
A sober look at the unsupervised learning of disentangled representations and their evaluation
F. Locatello, S. Bauer, M. Lucic, G. Rätsch, S. Gelly, B. Schölkopf, O. Bachem, Journal of Machine Learning Research 21 (2020).
[Published Version] View | Download Published Version (ext.) | arXiv
 
[17]
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:, Advances in Neural Information Processing Systems, Curran Associates, 2020, pp. 11525–11538.
[Preprint] View | Download Preprint (ext.) | arXiv
 
[16]
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
 
[15]
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
 
[14]
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
 
[13]
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
 
[12]
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
 
[11]
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
 
[10]
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
 
[9]
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
 
[8]
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
 
[7]
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
 
[6]
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
 
[5]
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
 
[4]
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
 
[3]
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
 
[2]
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
 
[1]
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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