| Are GANs Created Equal? A Large-Scale Study M Lucic, K Kurach, M Michalski, S Gelly, O Bousquet Advances in Neural Information Processing Systems, 2017 | 633 | 2017 |
| Challenging common assumptions in the unsupervised learning of disentangled representations F Locatello, S Bauer, M Lucic, S Gelly, B Schölkopf, O Bachem International Conference on Machine Learning (Best Paper Award), 2019 | 486 | 2019 |
| A Large-Scale Study on Regularization and Normalization in GANs K Kurach*, M Lucic*, X Zhai, M Michalski, S Gelly International Conference on Machine Learning, 2018 | 202* | 2018 |
| Assessing Generative Models via Precision and Recall MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly Advances in Neural Information Processing Systems, 2018 | 165 | 2018 |
| Recent advances in autoencoder-based representation learning M Tschannen, O Bachem, M Lucic Workshop on Bayesian Deep Learning (NeurIPS 2018), 2018 | 163 | 2018 |
| On Mutual Information Maximization for Representation Learning M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic International Conference on Learning Representations, 2020 | 153 | 2020 |
| Self-Supervised GANs via Auxiliary Rotation Loss T Chen, X Zhai, M Ritter, M Lucic, N Houlsby Conference on Computer Vision and Pattern Recognition, 2019 | 152* | 2019 |
| Fast and provably good seedings for k-means O Bachem, M Lucic, H Hassani, A Krause Advances in Neural Information Processing Systems, 2016 | 120 | 2016 |
| Approximate K-Means++ in Sublinear Time O Bachem, M Lucic, SH Hassani, A Krause AAAI Conference on Artificial Intelligence, 2016 | 101 | 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, 2019 | 91 | 2019 |
| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... arXiv preprint arXiv:2011.03395, 2020 | 86 | 2020 |
| On Self Modulation for Generative Adversarial Networks T Chen, M Lucic, N Houlsby, S Gelly International Conference on Learning Representations, 2019 | 84 | 2019 |
| Practical coreset constructions for machine learning O Bachem, M Lucic, A Krause arXiv preprint arXiv:1703.06476, 2017 | 75 | 2017 |
| Coresets for Nonparametric Estimation - the Case of DP-Means O Bachem, M Lucic, A Krause International Conference on Machine Learning, 2015 | 62 | 2015 |
| Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures M Lucic, O Bachem, A Krause International Conference on Artificial Intelligence and Statistics, 2016 | 61 | 2016 |
| The visual task adaptation benchmark X Zhai, J Puigcerver, A Kolesnikov, P Ruyssen, C Riquelme, M Lucic, ... | 57* | 2019 |
| Deep Generative Models for Distribution-Preserving Lossy Compression M Tschannen, E Agustsson, M Lucic Advances in Neural Information Processing Systems, 2018 | 57 | 2018 |
| Training Gaussian mixture models at scale via coresets M Lucic, M Faulkner, A Krause, D Feldman The Journal of Machine Learning Research, 2017 | 56 | 2017 |
| Scalable k-means clustering via lightweight coresets O Bachem, M Lucic, A Krause International Conference on Knowledge Discovery & Data Mining, 2018 | 55 | 2018 |
| Are GANs Created Equal? M Lucic, K Kurach, M Michalski, S Gelly, O Bousquet A large-scale study. arXiv e-prints 2 (4), 2017 | 48 | 2017 |
Olivier BachemResearch Scientist, Google BrainVerified email at google.com
