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MLP-Mixer: An all-MLP Architecture for Vision


May 17, 2021
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy

* Fixed parameter counts in Table 1 

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What Do Neural Networks Learn When Trained With Random Labels?


Jun 18, 2020
Hartmut Maennel, Ibrahim Alabdulmohsin, Ilya Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers


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Predicting Neural Network Accuracy from Weights


Feb 26, 2020
Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, Ilya Tolstikhin


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When can unlabeled data improve the learning rate?


May 28, 2019
Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin, Ruth Urner


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Practical and Consistent Estimation of f-Divergences


May 27, 2019
Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya Tolstikhin


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GeNet: Deep Representations for Metagenomics


Jan 30, 2019
Mateo Rojas-Carulla, Ilya Tolstikhin, Guillermo Luque, Nicholas Youngblut, Ruth Ley, Bernhard Schölkopf


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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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Differentially Private Database Release via Kernel Mean Embeddings


May 31, 2018
Matej Balog, Ilya Tolstikhin, Bernhard Schölkopf

* 35th International Conference on Machine Learning (ICML 2018) 

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Wasserstein Auto-Encoders


Mar 12, 2018
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schoelkopf

* Fixed a typo in Algorithm 2 

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On the Latent Space of Wasserstein Auto-Encoders


Feb 11, 2018
Paul K. Rubenstein, Bernhard Schoelkopf, Ilya Tolstikhin


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Probabilistic Active Learning of Functions in Structural Causal Models


Jun 30, 2017
Paul K. Rubenstein, Ilya Tolstikhin, Philipp Hennig, Bernhard Schoelkopf

* 9 pages main text + 4 pages supplement 

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AdaGAN: Boosting Generative Models


May 24, 2017
Ilya Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf

* Updated with MNIST pictures and discussions + Unrolled GAN experiments 

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From optimal transport to generative modeling: the VEGAN cookbook


May 22, 2017
Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin, Carl-Johann Simon-Gabriel, Bernhard Schoelkopf


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Consistent Kernel Mean Estimation for Functions of Random Variables


Oct 19, 2016
Carl-Johann Simon-Gabriel, Adam Ścibior, Ilya Tolstikhin, Bernhard Schölkopf

* NIPS 2016 Proceedings (p. 1732-1740) 
* 17 pages including appendix 

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Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning


Feb 23, 2016
Ilya Tolstikhin, Nikita Zhivotovskiy, Gilles Blanchard

* Corrected error in Inequality (1) 

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Minimax Lower Bounds for Realizable Transductive Classification


Feb 09, 2016
Ilya Tolstikhin, David Lopez-Paz


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Towards a Learning Theory of Cause-Effect Inference


May 18, 2015
David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya Tolstikhin


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Localized Complexities for Transductive Learning


Nov 26, 2014
Ilya Tolstikhin, Gilles Blanchard, Marius Kloft

* Appeared in Conference on Learning Theory 2014 

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