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
2842019
Recent advances in autoencoder-based representation learning
M Tschannen, O Bachem, M Lucic
arXiv preprint arXiv:1812.05069, 2018
1072018
Fast and provably good seedings for k-means
O Bachem, M Lucic, H Hassani, A Krause
Advances in neural information processing systems 29, 55-63, 2016
1072016
Assessing generative models via precision and recall
MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly
Advances in Neural Information Processing Systems, 5228-5237, 2018
1002018
K-mc2: Approximate k-means++ in sublinear time
O Bachem, M Lucic, H Hassani, A Krause
AAAI 2016, 2016
872016
High-fidelity image generation with fewer labels
M Lucic, M Tschannen, M Ritter, X Zhai, O Bachem, S Gelly
arXiv preprint arXiv:1903.02271, 2019
632019
Practical coreset constructions for machine learning
O Bachem, M Lucic, A Krause
arXiv preprint arXiv:1703.06476, 2017
572017
Scalable k-means clustering via lightweight coresets
O Bachem, M Lucic, A Krause
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018
562018
Coresets for Nonparametric Estimation-the Case of DP-Means.
O Bachem, M Lucic, A Krause
ICML, 209-217, 2015
562015
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
542016
On the fairness of disentangled representations
F Locatello, G Abbati, T Rainforth, S Bauer, B Schölkopf, O Bachem
Advances in Neural Information Processing Systems, 14611-14624, 2019
362019
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
S van Steenkiste, F Locatello, J Schmidhuber, O Bachem
Advances in Neural Information Processing Systems, 14245-14258, 2019
312019
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
282019
The visual task adaptation benchmark
X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ...
arXiv preprint arXiv:1910.04867, 2019
232019
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
192020
On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
MW Gondal, M Wuthrich, D Miladinovic, F Locatello, M Breidt, V Volchkov, ...
Advances in Neural Information Processing Systems, 15740-15751, 2019
182019
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
162017
One-shot coresets: The case of k-clustering
O Bachem, M Lucic, S Lattanzi
International conference on artificial intelligence and statistics, 784-792, 2018
152018
Weakly-Supervised Disentanglement Without Compromises
F Locatello, B Poole, G Rätsch, B Schölkopf, O Bachem, M Tschannen
arXiv preprint arXiv:2002.02886, 2020
142020
Linear-time outlier detection via sensitivity
M Lucic, O Bachem, A Krause
arXiv preprint arXiv:1605.00519, 2016
132016
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Articles 1–20