| Adagan: Boosting generative models IO Tolstikhin, S Gelly, O Bousquet, CJ Simon-Gabriel, B Schölkopf Advances in neural information processing systems 30, 2017 | 283 | 2017 |
| From optimal transport to generative modeling: the VEGAN cookbook O Bousquet, S Gelly, I Tolstikhin, CJ Simon-Gabriel, B Schoelkopf arXiv preprint arXiv:1705.07642, 2017 | 165 | 2017 |
| First-order adversarial vulnerability of neural networks and input dimension CJ Simon-Gabriel, Y Ollivier, L Bottou, B Schölkopf, D Lopez-Paz International conference on machine learning, 5809-5817, 2019 | 126 | 2019 |
| Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions CJ Simon-Gabriel, B Schölkopf Journal of Machine Learning Research 19 (44), 1-29, 2018 | 121 | 2018 |
| Prediction of human population responses to toxic compounds by a collaborative competition F Eduati, LM Mangravite, T Wang, H Tang, JC Bare, R Huang, T Norman, ... Nature biotechnology 33 (9), 933-940, 2015 | 121 | 2015 |
| Bridging the gap to real-world object-centric learning M Seitzer, M Horn, A Zadaianchuk, D Zietlow, T Xiao, CJ Simon-Gabriel, ... arXiv preprint arXiv:2209.14860, 2022 | 119 | 2022 |
| Modeling confounding by half-sibling regression B Schölkopf, DW Hogg, D Wang, D Foreman-Mackey, D Janzing, ... Proceedings of the National Academy of Sciences 113 (27), 7391-7398, 2016 | 76 | 2016 |
| Assaying out-of-distribution generalization in transfer learning F Wenzel, A Dittadi, P Gehler, CJ Simon-Gabriel, M Horn, D Zietlow, ... Advances in Neural Information Processing Systems 35, 7181-7198, 2022 | 71 | 2022 |
| Adversarial vulnerability of neural networks increases with input dimension CJ Simon-Gabriel, Y Ollivier, L Bottou, B Schölkopf, D Lopez-Paz | 49 | 2018 |
| Metrizing weak convergence with maximum mean discrepancies CJ Simon-Gabriel, A Barp, B Schölkopf, L Mackey Journal of Machine Learning Research 24 (184), 1-20, 2023 | 48 | 2023 |
| Targeted separation and convergence with kernel discrepancies A Barp, CJ Simon-Gabriel, M Girolami, L Mackey | 13 | 2022 |
| Removing systematic errors for exoplanet search via latent causes B Schölkopf, D Hogg, D Wang, D Foreman-Mackey, D Janzing, ... International Conference on Machine Learning, 2218-2226, 2015 | 12 | 2015 |
| Consistent kernel mean estimation for functions of random variables CJ Simon-Gabriel, A Scibior, I Tolstikhin, B Schölkopf Advances in Neural Information Processing Systems, 1732-1740, 2016 | 10 | 2016 |
| Object-centric multiple object tracking Z Zhao, J Wang, M Horn, Y Ding, T He, Z Bai, D Zietlow, CJ Simon-Gabriel, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 9 | 2023 |
| Unsupervised Open-Vocabulary Object Localization in Videos K Fan, Z Bai, T Xiao, D Zietlow, M Horn, Z Zhao, CJ Simon-Gabriel, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 6 | 2023 |
| Bootstrat: population informed bootstrapping for rare variant tests H Huang, GM Peloso, D Howrigan, B Rakitsch, CJ Simon-Gabriel, ... bioRxiv, 068999, 2016 | 5 | 2016 |
| Popskipjump: Decision-based attack for probabilistic classifiers CJ Simon-Gabriel, NA Sheikh, A Krause International Conference on Machine Learning, 9712-9721, 2021 | 4 | 2021 |
| Robust NAS under adversarial training: benchmark, theory, and beyond Y Wu, F Liu, CJ Simon-Gabriel, GG Chrysos, V Cevher arXiv preprint arXiv:2403.13134, 2024 | 2 | 2024 |
| Distribution-dissimilarities in machine learning CJ Simon-Gabriel Universität Tübingen, 2019 | 2 | 2019 |
| Robust NAS benchmark under adversarial training: benchmark, theory, and beyond Y Wu, F Liu, CJ Simon-Gabriel, G Chrysos, V Cevher Proceedings of ICLR, 2024 | | 2024 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
