Ilya Tolstikhin
Title
Cited by
Cited by
Year
Wasserstein auto-encoders
I Tolstikhin, O Bousquet, S Gelly, B Schoelkopf
arXiv preprint arXiv:1711.01558, 426-433, 2017
3742017
Adagan: Boosting generative models
IO Tolstikhin, S Gelly, O Bousquet, CJ Simon-Gabriel, B Schölkopf
Advances in Neural Information Processing Systems, 5424-5433, 2017
1362017
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
1052015
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
762017
PAC-Bayes-empirical-Bernstein inequality
IO Tolstikhin, Y Seldin
Advances in Neural Information Processing Systems, 109-117, 2013
382013
Minimax estimation of kernel mean embeddings
I Tolstikhin, BK Sriperumbudur, K Muandet
The Journal of Machine Learning Research 18 (1), 3002-3048, 2017
272017
Minimax estimation of maximum mean discrepancy with radial kernels
IO Tolstikhin, BK Sriperumbudur, B Schölkopf
Advances in Neural Information Processing Systems, 1930-1938, 2016
182016
On the latent space of wasserstein auto-encoders
PK Rubenstein, B Schoelkopf, I Tolstikhin
arXiv preprint arXiv:1802.03761, 2018
162018
Localized complexities for transductive learning
I Tolstikhin, G Blanchard, M Kloft
Conference on Learning Theory, 857-884, 2014
142014
Differentially private database release via kernel mean embeddings
M Balog, I Tolstikhin, B Schölkopf
International Conference on Machine Learning, 414-422, 2018
122018
Permutational rademacher complexity
I Tolstikhin, N Zhivotovskiy, G Blanchard
International Conference on Algorithmic Learning Theory, 209-223, 2015
92015
Clustering meets implicit generative models
F Locatello, D Vincent, I Tolstikhin, G Ratsch, S Gelly, B Scholkopf
72018
Consistent kernel mean estimation for functions of random variables
CJ Simon-Gabriel, A Scibior, IO Tolstikhin, B Schölkopf
Advances in Neural Information Processing Systems, 1732-1740, 2016
72016
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
62018
Learning disentangled representations with wasserstein auto-encoders
PK Rubenstein, B Schölkopf, I Tolstikhin
62018
Practical and consistent estimation of f-divergences
P Rubenstein, O Bousquet, J Djolonga, C Riquelme, IO Tolstikhin
Advances in Neural Information Processing Systems, 4070-4080, 2019
52019
Concentration inequalities for samples without replacement
IO Tolstikhin
Theory of Probability & Its Applications 61 (3), 462-481, 2017
52017
Genet: Deep representations for metagenomics
M Rojas-Carulla, I Tolstikhin, G Luque, N Youngblut, R Ley, B Schölkopf
arXiv preprint arXiv:1901.11015, 2019
42019
Probabilistic active learning of functions in structural causal models
PK Rubenstein, I Tolstikhin, P Hennig, B Schölkopf
arXiv preprint arXiv:1706.10234, 2017
32017
B0 matrix shim array design-optimization of the position, geometry and the number of segments of individual coil elements
I Zivkovic, I Tolstikhin, B Schoelkopf, K Scheffler
32016
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