Ilya Tolstikhin
Ilya Tolstikhin
Google
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Titel
Zitiert von
Zitiert von
Jahr
Wasserstein auto-encoders
I Tolstikhin, O Bousquet, S Gelly, B Schoelkopf
arXiv preprint arXiv:1711.01558, 426-433, 2017
6022017
Adagan: Boosting generative models
I Tolstikhin, S Gelly, O Bousquet, CJ Simon-Gabriel, B Schölkopf
arXiv preprint arXiv:1701.02386, 2017
1782017
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
1362015
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
992017
PAC-Bayes-empirical-Bernstein inequality
I Tolstikhin, Y Seldin
Advances in Neural Information Processing Systems 26 (NIPS 2013), 1-9, 2013
472013
Minimax estimation of kernel mean embeddings
I Tolstikhin, BK Sriperumbudur, K Muandet
The Journal of Machine Learning Research 18 (1), 3002-3048, 2017
362017
Minimax estimation of maximal mean discrepancy with radial kernels
I Tolstikhin, BK Sriperumbudur, B Schölkopf
Advances in Neural Information Processing Systems (NIPS), 1930-1938, 2016
282016
On the latent space of wasserstein auto-encoders
PK Rubenstein, B Schoelkopf, I Tolstikhin
arXiv preprint arXiv:1802.03761, 2018
272018
Differentially private database release via kernel mean embeddings
M Balog, I Tolstikhin, B Schölkopf
International Conference on Machine Learning, 414-422, 2018
222018
Practical and consistent estimation of f-divergences
PK Rubenstein, O Bousquet, J Djolonga, C Riquelme, I Tolstikhin
arXiv preprint arXiv:1905.11112, 2019
202019
Mlp-mixer: An all-mlp architecture for vision
I Tolstikhin, N Houlsby, A Kolesnikov, L Beyer, X Zhai, T Unterthiner, ...
arXiv preprint arXiv:2105.01601, 2021
162021
Localized complexities for transductive learning
I Tolstikhin, G Blanchard, M Kloft
Conference on Learning Theory, 857-884, 2014
142014
Learning disentangled representations with wasserstein auto-encoders
PK Rubenstein, B Schölkopf, I Tolstikhin
132018
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
122018
Permutational rademacher complexity
I Tolstikhin, N Zhivotovskiy, G Blanchard
International Conference on Algorithmic Learning Theory, 209-223, 2015
122015
Predicting neural network accuracy from weights
T Unterthiner, D Keysers, S Gelly, O Bousquet, I Tolstikhin
arXiv preprint arXiv:2002.11448, 2020
112020
Genet: Deep representations for metagenomics
M Rojas-Carulla, I Tolstikhin, G Luque, N Youngblut, R Ley, B Schölkopf
arXiv preprint arXiv:1901.11015, 2019
112019
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
92019
Probabilistic active learning of functions in structural causal models
PK Rubenstein, I Tolstikhin, P Hennig, B Schölkopf
arXiv preprint arXiv:1706.10234, 2017
82017
Concentration inequalities for samples without replacement
IO Tolstikhin
Theory of Probability & Its Applications 61 (3), 462-481, 2017
82017
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