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Towards Causal Representation Learning


Feb 22, 2021
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio

* Special Issue of Proceedings of the IEEE - Advances in Machine Learning and Deep Neural Networks 

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On the Transfer of Disentangled Representations in Realistic Settings


Oct 27, 2020
Andrea Dittadi, Frederik TrĂ€uble, Francesco Locatello, Manuel WĂŒthrich, Vaibhav Agrawal, Ole Winther, Stefan Bauer, Bernhard Schölkopf


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A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation


Oct 27, 2020
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar RÀtsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

* Journal of Machine Learning Research 2020, Volume 21, Number 209 
* arXiv admin note: substantial text overlap with arXiv:1811.12359 

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A Commentary on the Unsupervised Learning of Disentangled Representations


Jul 28, 2020
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar RÀtsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

* The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020 (AAAI-20) 

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Object-Centric Learning with Slot Attention


Jun 26, 2020
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf


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Is Independence all you need? On the Generalization of Representations Learned from Correlated Data


Jun 14, 2020
Frederik TrÀuble, Elliot Creager, Niki Kilbertus, Anirudh Goyal, Francesco Locatello, Bernhard Schölkopf, Stefan Bauer

* 33 pages, 33 figures 

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Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization


Feb 29, 2020
Geoffrey NĂ©giar, Gideon Dresdner, Alicia Tsai, Laurent El Ghaoui, Francesco Locatello, Fabian Pedregosa


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Weakly-Supervised Disentanglement Without Compromises


Feb 07, 2020
Francesco Locatello, Ben Poole, Gunnar RÀtsch, Bernhard Schölkopf, Olivier Bachem, Michael Tschannen


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On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset


Jun 07, 2019
Muhammad Waleed Gondal, Manuel WĂŒthrich, Đorđe Miladinović, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer


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On the Fairness of Disentangled Representations


May 31, 2019
Francesco Locatello, Gabriele Abbati, Tom Rainforth, Stefan Bauer, Bernhard Schölkopf, Olivier Bachem


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Are Disentangled Representations Helpful for Abstract Visual Reasoning?


May 29, 2019
Sjoerd van Steenkiste, Francesco Locatello, JĂŒrgen Schmidhuber, Olivier Bachem

* This is a preliminary pre-print 

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The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA


May 16, 2019
Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou, Francesco Locatello, Bernhard Schölkopf


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Disentangling Factors of Variation Using Few Labels


May 03, 2019
Francesco Locatello, Michael Tschannen, Stefan Bauer, Gunnar RÀtsch, Bernhard Schölkopf, Olivier Bachem


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Stochastic Conditional Gradient Method for Composite Convex Minimization


Jan 29, 2019
Francesco Locatello, Alp Yurtsever, Olivier Fercoq, Volkan Cevher


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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations


Dec 02, 2018
Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

* This is a preliminary preprint based on our initial experimental results 

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Boosting Black Box Variational Inference


Nov 01, 2018
Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar RĂ€tsch


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Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series


Oct 05, 2018
Vincent Fortuin, Matthias HĂŒser, Francesco Locatello, Heiko Strathmann, Gunnar RĂ€tsch


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Clustering Meets Implicit Generative Models


Aug 02, 2018
Francesco Locatello, Damien Vincent, Ilya Tolstikhin, Gunnar RÀtsch, Sylvain Gelly, Bernhard Schölkopf


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On Matching Pursuit and Coordinate Descent


Jul 02, 2018
Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar RÀtsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi

* ICML 2018 - Proceedings of the 35th International Conference on Machine Learning 

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Boosting Variational Inference: an Optimization Perspective


Mar 07, 2018
Francesco Locatello, Rajiv Khanna, Joydeep Ghosh, Gunnar RĂ€tsch

* AISTATS 2018 

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Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees


Nov 19, 2017
Francesco Locatello, Michael Tschannen, Gunnar RĂ€tsch, Martin Jaggi

* NIPS 2017 

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A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe


Mar 07, 2017
Francesco Locatello, Rajiv Khanna, Michael Tschannen, Martin Jaggi

* appearing at AISTATS 2017 

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