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ORCIDIlya O. Tolstikhin
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2020 – today
- 2023
[c17]Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin:
Fine-Grained Distribution-Dependent Learning Curves. COLT 2023: 5890-5924- 2022
- [i16]Olivier Bousquet, Steve Hanneke, Shay Moran, Jonathan Shafer, Ilya O. Tolstikhin:
Fine-Grained Distribution-Dependent Learning Curves. CoRR abs/2208.14615 (2022) - 2021
- [c16]Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy:
MLP-Mixer: An all-MLP Architecture for Vision. NeurIPS 2021: 24261-24272 - [i15]Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy:
MLP-Mixer: An all-MLP Architecture for Vision. CoRR abs/2105.01601 (2021) - [i14]Ibrahim M. Alabdulmohsin, Larisa Markeeva, Daniel Keysers, Ilya O. Tolstikhin:
A Generalized Lottery Ticket Hypothesis. CoRR abs/2107.06825 (2021) - 2020
- [c15]Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers:
What Do Neural Networks Learn When Trained With Random Labels? NeurIPS 2020 - [i13]Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, Ilya O. Tolstikhin:
Predicting Neural Network Accuracy from Weights. CoRR abs/2002.11448 (2020) - [i12]Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert J. N. Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers:
What Do Neural Networks Learn When Trained With Random Labels? CoRR abs/2006.10455 (2020)
2010 – 2019
- 2019
- [c14]Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner:
When can unlabeled data improve the learning rate? COLT 2019: 1500-1518 - [c13]Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin:
Practical and Consistent Estimation of f-Divergences. NeurIPS 2019: 4072-4082 - [i11]Mateo Rojas-Carulla, Ilya O. Tolstikhin, Guillermo Luque, Nicholas D. Youngblut, Ruth E. Ley, Bernhard Schölkopf:
GeNet: Deep Representations for Metagenomics. CoRR abs/1901.11015 (2019) - [i10]Paul K. Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya O. Tolstikhin:
Practical and Consistent Estimation of f-Divergences. CoRR abs/1905.11112 (2019) - [i9]Christina Göpfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, Ruth Urner:
When can unlabeled data improve the learning rate? CoRR abs/1905.11866 (2019) - 2018
- [c12]Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf:
Clustering Meets Implicit Generative Models. ICLR (Workshop) 2018 - [c11]Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin:
Learning Disentangled Representations with Wasserstein Auto-Encoders. ICLR (Workshop) 2018 - [c10]Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin:
Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders. ICLR (Workshop) 2018 - [c9]Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf:
Wasserstein Auto-Encoders. ICLR 2018 - [c8]Matej Balog, Ilya O. Tolstikhin, Bernhard Schölkopf:
Differentially Private Database Release via Kernel Mean Embeddings. ICML 2018: 423-431 - [i8]Paul K. Rubenstein, Bernhard Schölkopf, Ilya O. Tolstikhin:
On the Latent Space of Wasserstein Auto-Encoders. CoRR abs/1802.03761 (2018) - [i7]Francesco Locatello, Damien Vincent, Ilya O. Tolstikhin, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf:
Clustering Meets Implicit Generative Models. CoRR abs/1804.11130 (2018) - 2017
- [j1]Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Krikamol Muandet:
Minimax Estimation of Kernel Mean Embeddings. J. Mach. Learn. Res. 18: 86:1-86:47 (2017) - [c7]Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf:
AdaGAN: Boosting Generative Models. NIPS 2017: 5424-5433 - [i6]Ilya O. Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, Bernhard Schölkopf:
AdaGAN: Boosting Generative Models. CoRR abs/1701.02386 (2017) - [i5]Paul K. Rubenstein, Ilya O. Tolstikhin, Philipp Hennig, Bernhard Schölkopf:
Probabilistic Active Learning of Functions in Structural Causal Models. CoRR abs/1706.10234 (2017) - [i4]Ilya O. Tolstikhin, Olivier Bousquet, Sylvain Gelly, Bernhard Schölkopf:
Wasserstein Auto-Encoders. CoRR abs/1711.01558 (2017) - 2016
- [c6]Carl-Johann Simon-Gabriel, Adam Scibior, Ilya O. Tolstikhin, Bernhard Schölkopf:
Consistent Kernel Mean Estimation for Functions of Random Variables. NIPS 2016: 1732-1740 - [c5]Ilya O. Tolstikhin, Bharath K. Sriperumbudur, Bernhard Schölkopf:
Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels. NIPS 2016: 1930-1938 - [i3]Ilya O. Tolstikhin, David Lopez-Paz:
Minimax Lower Bounds for Realizable Transductive Classification. CoRR abs/1602.03027 (2016) - 2015
- [c4]Ilya O. Tolstikhin, Nikita Zhivotovskiy, Gilles Blanchard:
Permutational Rademacher Complexity - A New Complexity Measure for Transductive Learning. ALT 2015: 209-223 - [c3]David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, Ilya O. Tolstikhin:
Towards a Learning Theory of Cause-Effect Inference. ICML 2015: 1452-1461 - [i2]Ilya O. Tolstikhin, Nikita Zhivotovskiy, Gilles Blanchard:
Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning. CoRR abs/1505.02910 (2015) - 2014
- [c2]Ilya O. Tolstikhin, Gilles Blanchard, Marius Kloft:
Localized Complexities for Transductive Learning. COLT 2014: 857-884 - [i1]Ilya O. Tolstikhin, Gilles Blanchard, Marius Kloft:
Localized Complexities for Transductive Learning. CoRR abs/1411.7200 (2014) - 2013
- [c1]Ilya O. Tolstikhin, Yevgeny Seldin:
PAC-Bayes-Empirical-Bernstein Inequality. NIPS 2013: 109-117
Coauthor Index
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