Следене
Ilya Tolstikhin
Ilya Tolstikhin
Google
Потвърден имейл адрес: google.com - Начална страница
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Позовавания
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Mlp-mixer: An all-mlp architecture for vision
IO Tolstikhin, N Houlsby, A Kolesnikov, L Beyer, X Zhai, T Unterthiner, ...
Advances in neural information processing systems 34, 24261-24272, 2021
11382021
Wasserstein auto-encoders
I Tolstikhin, O Bousquet, S Gelly, B Schoelkopf
arXiv preprint arXiv:1711.01558, 426-433, 2017
10412017
Adagan: Boosting generative models
IO Tolstikhin, S Gelly, O Bousquet, CJ Simon-Gabriel, B Schölkopf
Advances in neural information processing systems 30, 2017
2542017
Towards a learning theory of cause-effect inference
D Lopez-Paz, K Muandet, B Schölkopf, I Tolstikhin
International Conference on Machine Learning, 1452-1461, 2015
1772015
From optimal transport to generative modeling: the VEGAN cookbook
O Bousquet, S Gelly, I Tolstikhin, CJ Simon-Gabriel, B Schoelkopf
URL http://arxiv. org/abs/1705.07642, 2017
1382017
Minimax estimation of maximum mean discrepancy with radial kernels
IO Tolstikhin, BK Sriperumbudur, B Schölkopf
Advances in Neural Information Processing Systems 29, 2016
922016
PAC-Bayes-empirical-Bernstein inequality
IO Tolstikhin, Y Seldin
Advances in Neural Information Processing Systems 26, 2013
682013
Minimax estimation of kernel mean embeddings
I Tolstikhin, BK Sriperumbudur, K Muandet
The Journal of Machine Learning Research 18 (1), 3002-3048, 2017
612017
On the latent space of wasserstein auto-encoders
PK Rubenstein, B Schoelkopf, I Tolstikhin
arXiv preprint arXiv:1802.03761, 2018
512018
What do neural networks learn when trained with random labels?
H Maennel, IM Alabdulmohsin, IO Tolstikhin, R Baldock, O Bousquet, ...
Advances in Neural Information Processing Systems 33, 19693-19704, 2020
502020
Predicting neural network accuracy from weights
T Unterthiner, D Keysers, S Gelly, O Bousquet, I Tolstikhin
arXiv preprint arXiv:2002.11448, 2020
442020
Practical and consistent estimation of f-divergences
P Rubenstein, O Bousquet, J Djolonga, C Riquelme, IO Tolstikhin
Advances in Neural Information Processing Systems 32, 2019
372019
Differentially private database release via kernel mean embeddings
M Balog, I Tolstikhin, B Schölkopf
International Conference on Machine Learning, 414-422, 2018
332018
When can unlabeled data improve the learning rate?
C Göpfert, S Ben-David, O Bousquet, S Gelly, I Tolstikhin, R Urner
Conference on Learning Theory, 1500-1518, 2019
242019
Learning disentangled representations with wasserstein auto-encoders
PK Rubenstein, B Schölkopf, I Tolstikhin
212018
Genet: Deep representations for metagenomics
M Rojas-Carulla, I Tolstikhin, G Luque, N Youngblut, R Ley, B Schölkopf
arXiv preprint arXiv:1901.11015, 2019
202019
Localized complexities for transductive learning
I Tolstikhin, G Blanchard, M Kloft
Conference on Learning Theory, 857-884, 2014
172014
Competitive training of mixtures of independent deep generative models
F Locatello, D Vincent, I Tolstikhin, G Rätsch, S Gelly, B Schölkopf
arXiv preprint arXiv:1804.11130, 2018
152018
Probabilistic active learning of functions in structural causal models
PK Rubenstein, I Tolstikhin, P Hennig, B Schölkopf
arXiv preprint arXiv:1706.10234, 2017
122017
Permutational Rademacher complexity: a new complexity measure for transductive learning
I Tolstikhin, N Zhivotovskiy, G Blanchard
Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff …, 2015
122015
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