I. Tolstikhin

发表

From optimal transport to generative modeling: the VEGAN cookbook

O. Bousquet, S. Gelly, B. Schoelkopf, 2017, 1705.07642.

Concentration Inequalities for Samples without Replacement

I. Tolstikhin, 2017 .

Predicting Neural Network Accuracy from Weights

O. Bousquet, S. Gelly, Thomas Unterthiner, 2020, ArXiv.

Towards a Learning Theory of Causation

David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, 2015 .

AdaGAN: Boosting Generative Models

Bernhard Schölkopf, Sylvain Gelly, Olivier Bousquet, 2017, NIPS.

A UTO-ENCODERS : LATENT DIMENSIONALITY AND RANDOM ENCODERS

Paul K. Rubenstein, B. Schölkopf, I. Tolstikhin, 2018 .

What Do Neural Networks Learn When Trained With Random Labels?

Ibrahim M. Alabdulmohsin, O. Bousquet, S. Gelly, 2020, NeurIPS.

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

Gilles Blanchard, Nikita Zhivotovskiy, Ilya O. Tolstikhin, 2015, ALT.

Combinatorial bounds on probability of overfitting based on clustering and coverage of classifiers∗

I. Tolstikhin, A. I. Frey, 2014 .

Learning Disentangled Representations with Wasserstein Auto-Encoders

Bernhard Schölkopf, Ilya O. Tolstikhin, Paul K. Rubenstein, 2018, ICLR.

Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders

Bernhard Schölkopf, Ilya O. Tolstikhin, Paul K. Rubenstein, 2018, ICLR.

B0 matrix shim array design-optimization of the position, geometry and the number of segments of individual coil elements

Bernhard Schölkopf, Klaus Scheffler, Ilya Tolstikhin, 2016 .

MLP-Mixer: An all-MLP Architecture for Vision

A. Dosovitskiy, Thomas Unterthiner, Jakob Uszkoreit, 2021, NeurIPS.

Fine-Grained Distribution-Dependent Learning Curves

O. Bousquet, Steve Hanneke, I. Tolstikhin, 2022, 2208.14615.

Fine-Grained Distribution-Dependent Learning Curves

O. Bousquet, Steve Hanneke, I. Tolstikhin, 2022, ArXiv.

GeNet: Deep Representations for Metagenomics

Bernhard Schölkopf, Mateo Rojas-Carulla, Ilya O. Tolstikhin, 2019, bioRxiv.

Predicting Neural Network Accuracy from Weights pdf

Daniel Keysers, Olivier Bousquet, Sylvain Gelly, 2020, ArXiv.

Competitive Training of Mixtures of Independent Deep Generative Models

Gunnar Rätsch, Bernhard Schölkopf, Francesco Locatello, 2018 .

Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels

Bernhard Schölkopf, Bharath K. Sriperumbudur, Ilya O. Tolstikhin, 2016, NIPS.

Minimax Lower Bounds for Realizable Transductive Classification pdf

David Lopez-Paz, Ilya O. Tolstikhin, I. Tolstikhin, 2016, ArXiv.

Consistent Kernel Mean Estimation for Functions of Random Variables

Bernhard Schölkopf, Adam Scibior, Ilya O. Tolstikhin, 2016, NIPS.

When can unlabeled data improve the learning rate?

Shai Ben-David, Ruth Urner, Olivier Bousquet, 2019, COLT.

PAC-Bayes-Empirical-Bernstein Inequality

Yevgeny Seldin, Ilya O. Tolstikhin, I. Tolstikhin, 2013, NIPS.

Practical and Consistent Estimation of f-Divergences

Carlos Riquelme, Olivier Bousquet, Josip Djolonga, 2019, NeurIPS.

Minimax Estimation of Kernel Mean Embeddings

Krikamol Muandet, Bharath K. Sriperumbudur, Ilya O. Tolstikhin, 2016, J. Mach. Learn. Res..

Towards a Learning Theory of Causation

Bernhard Schölkopf, David Lopez-Paz, Krikamol Muandet, 2015, 1502.02398.

On the Latent Space of Wasserstein Auto-Encoders pdf

Bernhard Schölkopf, Ilya O. Tolstikhin, Paul K. Rubenstein, 2018, ArXiv.

Towards a Learning Theory of Cause-Effect Inference

I. Tolstikhin, David Lopez-Paz, Krikamol Muandet, 2015, ICML.

Localized Complexities for Transductive Learning

Gilles Blanchard, Marius Kloft, Ilya O. Tolstikhin, 2014, COLT.

Differentially Private Database Release via Kernel Mean Embeddings

Bernhard Schölkopf, Matej Balog, Ilya O. Tolstikhin, 2017, ICML.

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

Alexander Kolesnikov, Xiaohua Zhai, Lucas Beyer, 2021, NeurIPS.

Probabilistic Active Learning of Functions in Structural Causal Models pdf

Bernhard Schölkopf, Philipp Hennig, Ilya O. Tolstikhin, 2017, ArXiv.