Ilya Tolstikhin
Ilya Tolstikhin
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TitelCiteras avÅr
Wasserstein auto-encoders
I Tolstikhin, O Bousquet, S Gelly, B Schoelkopf
arXiv preprint arXiv:1711.01558, 2017
2762017
Adagan: Boosting generative models
IO Tolstikhin, S Gelly, O Bousquet, CJ Simon-Gabriel, B Schölkopf
Advances in Neural Information Processing Systems, 5424-5433, 2017
1212017
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
1002015
From optimal transport to generative modeling: the VEGAN cookbook
O Bousquet, S Gelly, I Tolstikhin, CJ Simon-Gabriel, B Schoelkopf
arXiv preprint arXiv:1705.07642, 2017
592017
PAC-Bayes-empirical-Bernstein inequality
IO Tolstikhin, Y Seldin
Advances in Neural Information Processing Systems, 109-117, 2013
302013
Minimax estimation of kernel mean embeddings
I Tolstikhin, BK Sriperumbudur, K Muandet
The Journal of Machine Learning Research 18 (1), 3002-3048, 2017
212017
On the latent space of wasserstein auto-encoders
PK Rubenstein, B Schoelkopf, I Tolstikhin
arXiv preprint arXiv:1802.03761, 2018
162018
Minimax estimation of maximum mean discrepancy with radial kernels
IO Tolstikhin, BK Sriperumbudur, B Schölkopf
Advances in Neural Information Processing Systems, 1930-1938, 2016
152016
Localized complexities for transductive learning
I Tolstikhin, G Blanchard, M Kloft
Conference on Learning Theory, 857-884, 2014
132014
Differentially private database release via kernel mean embeddings
M Balog, I Tolstikhin, B Schölkopf
arXiv preprint arXiv:1710.01641, 2017
92017
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 Rätsch, S Gelly, B Schölkopf
arXiv preprint arXiv:1804.11130, 2018
72018
Wasserstein auto-encoders (2017)
I Tolstikhin, O Bousquet, S Gelly, B Schoelkopf
arXiv preprint arXiv:1711.01558, 426-433, 0
7
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
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
62016
From optimal transport to generative modeling: the VEGAN cookbook. 2017
O Bousquet, S Gelly, I Tolstikhin, CJ Simon-Gabriel, B Schoelkopf
URL http://arxiv. org/abs/1705.07642, 0
5
Concentration inequalities for samples without replacement
IO Tolstikhin
Theory of Probability & Its Applications 61 (3), 462-481, 2017
42017
Practical and Consistent Estimation of f-Divergences
P Rubenstein, O Bousquet, J Djolonga, C Riquelme, IO Tolstikhin
Advances in Neural Information Processing Systems, 4072-4082, 2019
32019
Learning disentangled representations with wasserstein auto-encoders
PK Rubenstein, B Schölkopf, I Tolstikhin
32018
Probabilistic active learning of functions in structural causal models
PK Rubenstein, I Tolstikhin, P Hennig, B Schölkopf
arXiv preprint arXiv:1706.10234, 2017
32017
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Artiklar 1–20