Ilya O. Tolstikhin

According to our database, Ilya O. Tolstikhin authored at least 24 papers between 2013 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Fine-Grained Distribution-Dependent Learning Curves.

Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2021

A Generalized Lottery Ticket Hypothesis.

Ibrahim M. Alabdulmohsin, Larisa Markeeva, Daniel Keysers, Ilya O. Tolstikhin

CoRR, 2021

MLP-Mixer: An all-MLP Architecture for Vision.

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Predicting Neural Network Accuracy from Weights.

Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, Ilya O. Tolstikhin

CoRR, 2020

What Do Neural Networks Learn When Trained With Random Labels?

Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

GeNet: Deep Representations for Metagenomics.

Mateo Rojas-Carulla, Ilya O. Tolstikhin, Guillermo Luque, Nicholas D. Youngblut, Ruth E. Ley, Bernhard Schölkopf

CoRR, 2019

Practical and Consistent Estimation of f-Divergences.

Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

When can unlabeled data improve the learning rate?

Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner

Proceedings of the Conference on Learning Theory, 2019

2018

On the Latent Space of Wasserstein Auto-Encoders.

Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin

CoRR, 2018

Differentially Private Database Release via Kernel Mean Embeddings.

Matej Balog, Ilya O. Tolstikhin, Bernhard Schölkopf

Proceedings of the 35th International Conference on Machine Learning, 2018

Wasserstein Auto-Encoders.

Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf

Proceedings of the 6th International Conference on Learning Representations, 2018

Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders.

Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin

Proceedings of the 6th International Conference on Learning Representations, 2018

Learning Disentangled Representations with Wasserstein Auto-Encoders.

Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin

Proceedings of the 6th International Conference on Learning Representations, 2018

Clustering Meets Implicit Generative Models.

Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Minimax Estimation of Kernel Mean Embeddings.

Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Krikamol Muandet

J. Mach. Learn. Res., 2017

Probabilistic Active Learning of Functions in Structural Causal Models.

Paul K. Rubenstein, Ilya O. Tolstikhin, Philipp Hennig, Bernhard Schölkopf

CoRR, 2017

AdaGAN: Boosting Generative Models.

Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Minimax Lower Bounds for Realizable Transductive Classification.

Ilya O. Tolstikhin, David Lopez-Paz

CoRR, 2016

Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels.

Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Consistent Kernel Mean Estimation for Functions of Random Variables.

Carl-Johann Simon-Gabriel, Adam Scibior, Ilya O. Tolstikhin, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Towards a Learning Theory of Cause-Effect Inference.

David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya O. Tolstikhin

Proceedings of the 32nd International Conference on Machine Learning, 2015

Permutational Rademacher Complexity - A New Complexity Measure for Transductive Learning.

Ilya O. Tolstikhin, Nikita Zhivotovskiy, Gilles Blanchard

Proceedings of the Algorithmic Learning Theory - 26th International Conference, 2015

2014

Localized Complexities for Transductive Learning.

Ilya O. Tolstikhin, Gilles Blanchard, Marius Kloft

Proceedings of The 27th Conference on Learning Theory, 2014

2013

PAC-Bayes-Empirical-Bernstein Inequality.

Ilya O. Tolstikhin, Yevgeny Seldin

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013