Ilya O. Tolstikhin

34 papers A* 8B 1Journal 17Unranked 8
YearRankTypeTitle / Venue / Authors
2023 A* conf
Fine-Grained Distribution-Dependent Learning Curves.
COLT
Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin
2022 J jnl
Fine-Grained Distribution-Dependent Learning Curves.
CoRR
Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin
2021 J jnl
A Generalized Lottery Ticket Hypothesis.
CoRR
Ibrahim M. Alabdulmohsin, Larisa Markeeva, Daniel Keysers, Ilya O. Tolstikhin
2021 A* conf
MLP-Mixer: An all-MLP Architecture for Vision.
NeurIPS
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
2021 J jnl
MLP-Mixer: An all-MLP Architecture for Vision.
CoRR
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
2020 J jnl
Predicting Neural Network Accuracy from Weights.
CoRR
Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, Ilya O. Tolstikhin
2020 A* conf
What Do Neural Networks Learn When Trained With Random Labels?
NeurIPS
Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers
2020 J jnl
What Do Neural Networks Learn When Trained With Random Labels?
CoRR
Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers
2019 J jnl
GeNet: Deep Representations for Metagenomics.
CoRR
Mateo Rojas-Carulla, Ilya O. Tolstikhin, Guillermo Luque, Nicholas D. Youngblut, Ruth E. Ley, Bernhard Schölkopf
2019 A* conf
Practical and Consistent Estimation of f-Divergences.
NeurIPS
Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin
2019 J jnl
Practical and Consistent Estimation of f-Divergences.
CoRR
Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin
2019 A* conf
When can unlabeled data improve the learning rate?
COLT
Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner
2019 J jnl
When can unlabeled data improve the learning rate?
CoRR
Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner
2018 — conf
Clustering Meets Implicit Generative Models.
ICLR (Workshop)
Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf
2018 J jnl
Clustering Meets Implicit Generative Models.
CoRR
Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf
2018 A* conf
Differentially Private Database Release via Kernel Mean Embeddings.
ICML
Matej Balog, Ilya O. Tolstikhin, Bernhard Schölkopf
2018 — conf
Learning Disentangled Representations with Wasserstein Auto-Encoders.
ICLR (Workshop)
Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin
2018 J jnl
On the Latent Space of Wasserstein Auto-Encoders.
CoRR
Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin
2018 — conf
Wasserstein Auto-Encoders.
ICLR
Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf
2018 — conf
Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders.
ICLR (Workshop)
Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin
2017 — conf
AdaGAN: Boosting Generative Models.
NIPS
Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf
2017 J jnl
AdaGAN: Boosting Generative Models.
CoRR
Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf
2017 J jnl
Minimax Estimation of Kernel Mean Embeddings.
J. Mach. Learn. Res.
Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Krikamol Muandet
2017 J jnl
Probabilistic Active Learning of Functions in Structural Causal Models.
CoRR
Paul K. Rubenstein, Ilya O. Tolstikhin, Philipp Hennig, Bernhard Schölkopf
2017 J jnl
Wasserstein Auto-Encoders.
CoRR
Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf
2016 — conf
Consistent Kernel Mean Estimation for Functions of Random Variables.
NIPS
Carl-Johann Simon-Gabriel, Adam Scibior, Ilya O. Tolstikhin, Bernhard Schölkopf
2016 — conf
Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels.
NIPS
Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Bernhard Schölkopf
2016 J jnl
Minimax Lower Bounds for Realizable Transductive Classification.
CoRR
Ilya O. Tolstikhin, David Lopez-Paz
2015 B conf
Permutational Rademacher Complexity - A New Complexity Measure for Transductive Learning.
ALT
Ilya O. Tolstikhin, Nikita Zhivotovskiy, Gilles Blanchard
2015 J jnl
Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning.
CoRR
Ilya O. Tolstikhin, Nikita Zhivotovskiy, Gilles Blanchard
2015 A* conf
Towards a Learning Theory of Cause-Effect Inference.
ICML
David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya O. Tolstikhin
2014 A* conf
Localized Complexities for Transductive Learning.
COLT
Ilya O. Tolstikhin, Gilles Blanchard, Marius Kloft
2014 J jnl
Localized Complexities for Transductive Learning.
CoRR
Ilya O. Tolstikhin, Gilles Blanchard, Marius Kloft
2013 — conf
PAC-Bayes-Empirical-Bernstein Inequality.
NIPS
Ilya O. Tolstikhin, Yevgeny Seldin