Ilya O. Tolstikhin

Publication Activity (10 Years)

Years Active: 2013-2023
Publications (10 Years): 28

Top Topics

Generative Model

Int J Imaging Syst Technol

Multi Layered Perceptron

Learning Curves

Top Venues

ICLR (Workshop)

Publications

Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin
Fine-Grained Distribution-Dependent Learning Curves. COLT (2023)
Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin
Fine-Grained Distribution-Dependent Learning Curves. CoRR (2022)
Ibrahim M. Alabdulmohsin, Larisa Markeeva, Daniel Keysers, Ilya O. Tolstikhin
A Generalized Lottery Ticket Hypothesis. CoRR (2021)
Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy
MLP-Mixer: An all-MLP Architecture for Vision. NeurIPS (2021)
Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy
MLP-Mixer: An all-MLP Architecture for Vision. CoRR (2021)
Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, Ilya O. Tolstikhin
Predicting Neural Network Accuracy from Weights. CoRR (2020)
Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers
What Do Neural Networks Learn When Trained With Random Labels? NeurIPS (2020)
Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers
What Do Neural Networks Learn When Trained With Random Labels? CoRR (2020)
Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin
Practical and Consistent Estimation of f-Divergences. NeurIPS (2019)
Mateo Rojas-Carulla, Ilya O. Tolstikhin, Guillermo Luque, Nicholas Youngblut, Ruth Ley, Bernhard Schölkopf
GeNet: Deep Representations for Metagenomics. CoRR (2019)
Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin
Practical and Consistent Estimation of f-Divergences. CoRR (2019)
Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner
When can unlabeled data improve the learning rate? CoRR (2019)
Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner
When can unlabeled data improve the learning rate? COLT (2019)
Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin
Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders. ICLR (Workshop) (2018)
Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf
Clustering Meets Implicit Generative Models. CoRR (2018)
Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin
On the Latent Space of Wasserstein Auto-Encoders. CoRR (2018)
Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin
Learning Disentangled Representations with Wasserstein Auto-Encoders. ICLR (Workshop) (2018)
Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf
Clustering Meets Implicit Generative Models. ICLR (Workshop) (2018)
Matej Balog, Ilya O. Tolstikhin, Bernhard Schölkopf
Differentially Private Database Release via Kernel Mean Embeddings. ICML (2018)
Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf
Wasserstein Auto-Encoders. ICLR (2018)
Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf
AdaGAN: Boosting Generative Models. CoRR (2017)
Paul K. Rubenstein, Ilya O. Tolstikhin, Philipp Hennig, Bernhard Schölkopf
Probabilistic Active Learning of Functions in Structural Causal Models. CoRR (2017)
Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf
Wasserstein Auto-Encoders. CoRR (2017)
Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Krikamol Muandet
Minimax Estimation of Kernel Mean Embeddings. J. Mach. Learn. Res. 18 (2017)
Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf
AdaGAN: Boosting Generative Models. NIPS (2017)
Ilya O. Tolstikhin, David Lopez-Paz
Minimax Lower Bounds for Realizable Transductive Classification. CoRR (2016)
Carl-Johann Simon-Gabriel, Adam Scibior, Ilya O. Tolstikhin, Bernhard Schölkopf
Consistent Kernel Mean Estimation for Functions of Random Variables. NIPS (2016)
Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Bernhard Schölkopf
Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels. NIPS (2016)
David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya O. Tolstikhin
Towards a Learning Theory of Cause-Effect Inference. ICML (2015)
Ilya O. Tolstikhin, Nikita Zhivotovskiy, Gilles Blanchard
Permutational Rademacher Complexity - A New Complexity Measure for Transductive Learning. ALT (2015)
Ilya O. Tolstikhin, Nikita Zhivotovskiy, Gilles Blanchard
Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning. CoRR (2015)
Ilya O. Tolstikhin, Gilles Blanchard, Marius Kloft
Localized Complexities for Transductive Learning. CoRR (2014)
Ilya O. Tolstikhin, Gilles Blanchard, Marius Kloft
Localized Complexities for Transductive Learning. COLT (2014)
Ilya O. Tolstikhin, Yevgeny Seldin
PAC-Bayes-Empirical-Bernstein Inequality. NIPS (2013)