| 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 | 282 | 2019 |
| 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 | 106 | 2016 |
| Recent advances in autoencoder-based representation learning M Tschannen, O Bachem, M Lucic arXiv preprint arXiv:1812.05069, 2018 | 103 | 2018 |
| 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 | 99 | 2018 |
| K-mc2: Approximate k-means++ in sublinear time O Bachem, M Lucic, H Hassani, A Krause AAAI 2016, 2016 | 87 | 2016 |
| 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 | 63 | 2019 |
| Practical coreset constructions for machine learning O Bachem, M Lucic, A Krause arXiv preprint arXiv:1703.06476, 2017 | 57 | 2017 |
| Scalable k-means clustering via lightweight coresets O Bachem, M Lucic, A Krause Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 56 | 2018 |
| Coresets for Nonparametric Estimation-the Case of DP-Means. O Bachem, M Lucic, A Krause ICML, 209-217, 2015 | 55 | 2015 |
| 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 | 54 | 2016 |
| 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 | 35 | 2019 |
| 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 | 30 | 2019 |
| 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 | 28 | 2019 |
| The visual task adaptation benchmark X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ... arXiv preprint arXiv:1910.04867, 2019 | 22 | 2019 |
| 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 | 18 | 2020 |
| 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 | 18 | 2019 |
| 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 | 16 | 2017 |
| One-shot coresets: The case of k-clustering O Bachem, M Lucic, S Lattanzi International conference on artificial intelligence and statistics, 784-792, 2018 | 15 | 2018 |
| Weakly-Supervised Disentanglement Without Compromises F Locatello, B Poole, G Rätsch, B Schölkopf, O Bachem, M Tschannen arXiv preprint arXiv:2002.02886, 2020 | 14 | 2020 |
| Linear-time outlier detection via sensitivity M Lucic, O Bachem, A Krause arXiv preprint arXiv:1605.00519, 2016 | 13 | 2016 |
Mario LučićSenior Research Scientist, Google BrainVerifierad e-postadress på google.com
