| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2183 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 684 | 2024 |
| On mutual information maximization for representation learning M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic arXiv preprint arXiv:1907.13625, 2019 | 579 | 2019 |
| Audiopalm: A large language model that can speak and listen PK Rubenstein, C Asawaroengchai, DD Nguyen, A Bapna, Z Borsos, ... arXiv preprint arXiv:2306.12925, 2023 | 152 | 2023 |
| Causal consistency of structural equation models PK Rubenstein, S Weichwald, S Bongers, JM Mooij, D Janzing, ... 33rd Conference on Uncertainty in Artificial Intelligence (UAI 2017), 2017 | 125 | 2017 |
| The incomplete rosetta stone problem: Identifiability results for multi-view nonlinear ica L Gresele, PK Rubenstein, A Mehrjou, F Locatello, B Schölkopf 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), 2019 | 92 | 2019 |
| On the latent space of wasserstein auto-encoders PK Rubenstein, B Schoelkopf, I Tolstikhin arXiv preprint arXiv:1802.03761, 2018 | 56 | 2018 |
| From deterministic ODEs to dynamic structural causal models PK Rubenstein, S Bongers, B Schölkopf, JM Mooij 34th Conference on Uncertainty in Artificial Intelligence (UAI 2018), 2016 | 54 | 2016 |
| Practical and Consistent Estimation of f-Divergences PK Rubenstein, O Bousquet, J Djolonga, C Riquelme, I Tolstikhin Advances in Neural Information Processing Systems, 2019, 2019 | 50 | 2019 |
| Slm: Bridge the thin gap between speech and text foundation models M Wang, W Han, I Shafran, Z Wu, CC Chiu, Y Cao, N Chen, Y Zhang, ... 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 1-8, 2023 | 34 | 2023 |
| Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks J von Kügelgen, PK Rubenstein, B Schölkopf, A Weller NeurIPS 2019 Workshop “Do the right thing”: Machine Learning and Causal …, 2019 | 30 | 2019 |
| Learning Disentangled Representations with Wasserstein Auto-Encoders PK Rubenstein, B Schölkopf, I Tolstikhin International Conference on Learning Representations (ICLR), Workshop Track …, 2018 | 28 | 2018 |
| Spatial consistency loss for training multi-label classifiers from single-label annotations T Verelst, PK Rubenstein, M Eichner, T Tuytelaars, M Berman Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 26 | 2023 |
| Wasserstein auto-encoders: Latent dimensionality and random encoders PK Rubenstein, B Schoelkopf, I Tolstikhin International Conference on Learning Representations (ICLR), Workshop Track …, 2018 | 13 | 2018 |
| Probabilistic Active Learning of Functions in Structural Causal Models PK Rubenstein, I Tolstikhin, P Hennig, B Schölkopf Causality Workshop of the 33rd Conference on Uncertainty in Artificial …, 2017 | 12 | 2017 |
| On mutual information maximization for representation learning. arXiv 2019 M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic arXiv preprint arXiv:1907.13625, 0 | 12 | |
| A kernel test for three-variable interactions with random processes PK Rubenstein, KP Chwialkowski, A Gretton 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016 | 8 | 2016 |
| Learning translation quality evaluation on low resource languages from large language models A Mohtashami, M Verzetti, PK Rubenstein arXiv preprint arXiv:2302.03491, 2023 | 6 | 2023 |
| On mutual information maximization for representation learning.[arXiv] M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic arXiv preprint arXiv:1907.13625, 2019 | 6 | 2019 |
| Structural causal models for macro-variables in time-series D Janzing, P Rubenstein, B Schölkopf arXiv preprint arXiv:1804.03911, 2018 | 6 | 2018 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
