Olivier Bachem
Olivier Bachem
Research Scientist, Google Brain
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Challenging common assumptions in the unsupervised learning of disentangled representations
F Locatello, S Bauer, M Lucic, G Raetsch, S Gelly, B Schölkopf, O Bachem
international conference on machine learning, 4114-4124, 2019
3272019
Recent advances in autoencoder-based representation learning
M Tschannen, O Bachem, M Lucic
arXiv preprint arXiv:1812.05069, 2018
1252018
Assessing generative models via precision and recall
MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly
arXiv preprint arXiv:1806.00035, 2018
1132018
Fast and Provably Good Seedings for k-Means.
O Bachem, M Lucic, SH Hassani, A Krause
Nips, 55-63, 2016
1042016
K-mc2: approximate k-means++ in sublinear time
O Bachem, M Lucic, H Hassani, A Krause
AAAI 2016, 2016
89*2016
High-fidelity image generation with fewer labels
M Lučić, M Tschannen, M Ritter, X Zhai, O Bachem, S Gelly
International Conference on Machine Learning, 4183-4192, 2019
712019
Practical coreset constructions for machine learning
O Bachem, M Lucic, A Krause
arXiv preprint arXiv:1703.06476, 2017
622017
Coresets for nonparametric estimation-the case of DP-means
O Bachem, M Lucic, A Krause
International Conference on Machine Learning, 209-217, 2015
582015
Strong coresets for hard and soft Bregman clustering with applications to exponential family mixtures
M Lucic, O Bachem, A Krause
Artificial intelligence and statistics, 1-9, 2016
572016
Are disentangled representations helpful for abstract visual reasoning?
S Van Steenkiste, F Locatello, J Schmidhuber, O Bachem
arXiv preprint arXiv:1905.12506, 2019
462019
On the fairness of disentangled representations
F Locatello, G Abbati, T Rainforth, S Bauer, B Schölkopf, O Bachem
arXiv preprint arXiv:1905.13662, 2019
442019
Scalable k-means clustering via lightweight coresets
O Bachem, M Lucic, A Krause
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018
442018
Disentangling factors of variation using few labels
F Locatello, M Tschannen, S Bauer, G Rätsch, B Schölkopf, O Bachem
arXiv preprint arXiv:1905.01258, 2019
332019
Google research football: A novel reinforcement learning environment
K Kurach, A Raichuk, P Stańczyk, M Zając, O Bachem, L Espeholt, ...
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4501-4510, 2020
252020
The visual task adaptation benchmark
X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ...
202019
Distributed and provably good seedings for k-means in constant rounds
O Bachem, M Lucic, A Krause
International Conference on Machine Learning, 292-300, 2017
202017
On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
MW Gondal, M Wüthrich, Đ Miladinović, F Locatello, M Breidt, V Volchkov, ...
arXiv preprint arXiv:1906.03292, 2019
192019
One-shot coresets: The case of k-clustering
O Bachem, M Lucic, S Lattanzi
International conference on artificial intelligence and statistics, 784-792, 2018
192018
Weakly-supervised disentanglement without compromises
F Locatello, B Poole, G Rätsch, B Schölkopf, O Bachem, M Tschannen
International Conference on Machine Learning, 6348-6359, 2020
162020
Scalable and distributed clustering via lightweight coresets
O Bachem, INF ETHZ, M Lucic, E CH
stat 1050, 27, 2017
162017
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