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.).

Shortcuts for causal discovery of nonlinear models by score matching
F. Montagna, N. Noceti, L. Rosasco, F. Locatello, ArXiv (n.d.).

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.).

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.

Causality in the time of LLMs: Round table discussion results of CLeaR 2023
C. Zhang, D. Janzing, M. van der Schaar, F. Locatello, P. Spirtes, K. Zhang, B. Schölkopf, C. Uhler, in:, 2nd Conference on Causal Learning and Reasoning, 2023.

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.

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.

 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.

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.

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.

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.

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.

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.

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.

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.

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.

Compositional multi-object reinforcement learning with linear relation networks
D. Mambelli, F. Träuble, S. Bauer, B. Schölkopf, F. Locatello, ArXiv (n.d.).

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.

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.).

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.

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.

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.

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.

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.

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.

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.

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.

Enforcing and discovering structure in machine learning
F. Locatello, ArXiv (n.d.).

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.

SCIM: Universal single-cell matching with unpaired feature sets
S.G. Stark, J. Ficek, F. Locatello, X. Bonilla, S. Chevrier, F. Singer, R. Aebersold, F.S. Al-Quaddoomi, J. Albinus, I. Alborelli, S. Andani, P.-O. Attinger, M. Bacac, D. Baumhoer, B. Beck-Schimmer, N. Beerenwinkel, C. Beisel, L. Bernasconi, A. Bertolini, B. Bodenmiller, X. Bonilla, R. Casanova, S. Chevrier, N. Chicherova, M. D’Costa, E. Danenberg, N. Davidson, M.-A.D. gan, R. Dummer, S. Engler, M. Erkens, K. Eschbach, C. Esposito, A. Fedier, P. Ferreira, J. Ficek, A.L. Frei, B. Frey, S. Goetze, L. Grob, G. Gut, D. Günther, M. Haberecker, P. Haeuptle, V. Heinzelmann-Schwarz, S. Herter, R. Holtackers, T. Huesser, A. Irmisch, F. Jacob, A. Jacobs, T.M. Jaeger, K. Jahn, A.R. James, P.M. Jermann, A. Kahles, A. Kahraman, V.H. Koelzer, W. Kuebler, J. Kuipers, C.P. Kunze, C. Kurzeder, K.-V. Lehmann, M. Levesque, S. Lugert, G. Maass, M. Manz, P. Markolin, J. Mena, U. Menzel, J.M. Metzler, N. Miglino, E.S. Milani, H. Moch, S. Muenst, R. Murri, C.K. Ng, S. Nicolet, M. Nowak, P.G. Pedrioli, L. Pelkmans, S. Piscuoglio, M. Prummer, M. Ritter, C. Rommel, M.L. Rosano-González, G. Rätsch, N. Santacroce, J.S. del Castillo, R. Schlenker, P.C. Schwalie, S. Schwan, T. Schär, G. Senti, F. Singer, S. Sivapatham, B. Snijder, B. Sobottka, V.T. Sreedharan, S. Stark, D.J. Stekhoven, A.P. Theocharides, T.M. Thomas, M. Tolnay, V. Tosevski, N.C. Toussaint, M.A. Tuncel, M. Tusup, A.V. Drogen, M. Vetter, T. Vlajnic, S. Weber, W.P. Weber, R. Wegmann, M. Weller, F. Wendt, N. Wey, A. Wicki, B. Wollscheid, S. Yu, J. Ziegler, M. Zimmermann, M. Zoche, G. Zuend, G. Rätsch, K.-V. Lehmann, Bioinformatics 36 (2020) i919–i927.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
M.W. Gondal, M. Wüthrich, . Miladinovi, F. Locatello, M. Breidt, V. Volchkov, J. Akpo, O. Bachem, B. Schölkopf, S. Bauer, in:, Advances in Neural Information Processing Systems, 2019.

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.

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.

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.

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.

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.

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.

Competitive training of mixtures of independent deep generative models
F. Locatello, D. Vincent, I. Tolstikhin, G. Rätsch, S. Gelly, B. Schölkopf, ArXiv (n.d.).

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.

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.