PAC-Bayes-Empirical-Bernstein Inequality. - Ilya O. Tolstikhin; Yevgeny Seldin (NIPS, 2013)

Localized Complexities for Transductive Learning. - Ilya O. Tolstikhin; Gilles Blanchard; Marius Kloft (COLT, 2014)

Permutational Rademacher Complexity - A New Complexity Measure for Transductive Learning. - Ilya O. Tolstikhin; Nikita Zhivotovskiy; Gilles Blanchard (ALT, 2015)

Towards a Learning Theory of Cause-Effect Inference. - David Lopez-Paz; Krikamol Muandet; Bernhard Schölkopf; Ilya O. Tolstikhin (ICML, 2015)

Consistent Kernel Mean Estimation for Functions of Random Variables. - Carl-Johann Simon-Gabriel; Adam Scibior; Ilya O. Tolstikhin; Bernhard Schölkopf (NIPS, 2016)

Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels. - Ilya O. Tolstikhin; Bharath K. Sriperumbudur; Bernhard Schölkopf (NIPS, 2016)

Minimax Lower Bounds for Realizable Transductive Classification. - Ilya O. Tolstikhin; David Lopez-Paz (CoRR, 2016)

AdaGAN: Boosting Generative Models. - Ilya O. Tolstikhin; Sylvain Gelly; Olivier Bousquet; Carl-Johann Simon-Gabriel; Bernhard Schölkopf (NIPS, 2017)

Probabilistic Active Learning of Functions in Structural Causal Models. - Paul K. Rubenstein; Ilya O. Tolstikhin; Philipp Hennig; Bernhard Schölkopf (CoRR, 2017)

Minimax Estimation of Kernel Mean Embeddings. - Ilya O. Tolstikhin; Bharath K. Sriperumbudur; Krikamol Muandet (J. Mach. Learn. Res., 2017)

Clustering Meets Implicit Generative Models. - Francesco Locatello; Damien Vincent; Ilya O. Tolstikhin; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf (ICLR (Workshop), 2018)

Learning Disentangled Representations with Wasserstein Auto-Encoders. - Paul K. Rubenstein; Bernhard Schölkopf; Ilya O. Tolstikhin (ICLR (Workshop), 2018)

Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders. - Paul K. Rubenstein; Bernhard Schölkopf; Ilya O. Tolstikhin (ICLR (Workshop), 2018)

Wasserstein Auto-Encoders. - Ilya O. Tolstikhin; Olivier Bousquet; Sylvain Gelly; Bernhard Schölkopf (ICLR, 2018)

Differentially Private Database Release via Kernel Mean Embeddings. - Matej Balog; Ilya O. Tolstikhin; Bernhard Schölkopf (ICML, 2018)

On the Latent Space of Wasserstein Auto-Encoders. - Paul K. Rubenstein; Bernhard Schölkopf; Ilya O. Tolstikhin (CoRR, 2018)

When can unlabeled data improve the learning rate? - Christina Göpfert; Shai Ben-David; Olivier Bousquet; Sylvain Gelly; Ilya O. Tolstikhin; Ruth Urner (COLT, 2019)

Practical and Consistent Estimation of f-Divergences. - Paul K. Rubenstein; Olivier Bousquet; Josip Djolonga; Carlos Riquelme; Ilya O. Tolstikhin (NeurIPS, 2019)

GeNet: Deep Representations for Metagenomics. - Mateo Rojas-Carulla; Ilya O. Tolstikhin; Guillermo Luque; Nicholas D. Youngblut; Ruth E. Ley; Bernhard Schölkopf (CoRR, 2019)

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 (NeurIPS, 2020)

Predicting Neural Network Accuracy from Weights. - Thomas Unterthiner; Daniel Keysers; Sylvain Gelly; Olivier Bousquet; Ilya O. Tolstikhin (CoRR, 2020)

MLP-Mixer: An all-MLP Architecture for Vision. - Ilya O. Tolstikhin; Neil Houlsby; Alexander Kolesnikov 003; Lucas Beyer; Xiaohua Zhai; Thomas Unterthiner; Jessica Yung; Andreas Steiner 001; Daniel Keysers; Jakob Uszkoreit; Mario Lucic; Alexey Dosovitskiy (NeurIPS, 2021)

A Generalized Lottery Ticket Hypothesis. - Ibrahim M. Alabdulmohsin; Larisa Markeeva; Daniel Keysers; Ilya O. Tolstikhin (CoRR, 2021)

Fine-Grained Distribution-Dependent Learning Curves. - Olivier Bousquet; Steve Hanneke; Shay Moran; Jonathan Shafer; Ilya O. Tolstikhin (COLT, 2023)