| 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 | 1149 | 2019 |
| Recent advances in autoencoder-based representation learning M Tschannen, O Bachem, M Lucic arXiv preprint arXiv:1812.05069, 2018 | 381 | 2018 |
| Assessing generative models via precision and recall MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly Advances in neural information processing systems 31, 2018 | 372 | 2018 |
| 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 | 211 | 2020 |
| 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 | 195 | 2020 |
| On the fairness of disentangled representations F Locatello, G Abbati, T Rainforth, S Bauer, B Schölkopf, O Bachem Advances in neural information processing systems 32, 2019 | 179 | 2019 |
| Fast and provably good seedings for k-means O Bachem, M Lucic, H Hassani, A Krause Advances in neural information processing systems 29, 2016 | 166 | 2016 |
| A large-scale study of representation learning with the visual task adaptation benchmark X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ... arXiv preprint arXiv:1910.04867, 2019 | 163 | 2019 |
| Are disentangled representations helpful for abstract visual reasoning? S Van Steenkiste, F Locatello, J Schmidhuber, O Bachem Advances in Neural Information Processing Systems 32, 2019 | 159 | 2019 |
| K-mc2: approximate k-means++ in sublinear time O Bachem, M Lucic, H Hassani, A Krause AAAI 2016, 2016 | 145* | 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 | 143 | 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 | 136 | 2019 |
| Practical coreset constructions for machine learning O Bachem, M Lucic, A Krause arXiv preprint arXiv:1703.06476, 2017 | 134 | 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 | 129 | 2018 |
| What matters in on-policy reinforcement learning? a large-scale empirical study M Andrychowicz, A Raichuk, P Stańczyk, M Orsini, S Girgin, R Marinier, ... arXiv preprint arXiv:2006.05990, 2020 | 127 | 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 32, 2019 | 99 | 2019 |
| Brax--A Differentiable Physics Engine for Large Scale Rigid Body Simulation CD Freeman, E Frey, A Raichuk, S Girgin, I Mordatch, O Bachem arXiv preprint arXiv:2106.13281, 2021 | 98 | 2021 |
| 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 | 92 | 2016 |
| Coresets for nonparametric estimation-the case of DP-means O Bachem, M Lucic, A Krause International Conference on Machine Learning, 209-217, 2015 | 86 | 2015 |
| What matters for on-policy deep actor-critic methods? a large-scale study M Andrychowicz, A Raichuk, P Stańczyk, M Orsini, S Girgin, R Marinier, ... International conference on learning representations, 2021 | 77 | 2021 |
Mario LučićResearch Scientist, Google BrainVerified email at google.com
