| On mutual information maximization for representation learning M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic arXiv preprint arXiv:1907.13625, 2019 | 475 | 2019 |
| 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 | 93 | 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 | 66 | 2019 |
| On the latent space of wasserstein auto-encoders PK Rubenstein, B Schoelkopf, I Tolstikhin arXiv preprint arXiv:1802.03761, 2018 | 54 | 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 | 42 | 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 | 41 | 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 | 30 | 2023 |
| Learning Disentangled Representations with Wasserstein Auto-Encoders PK Rubenstein, B Schölkopf, I Tolstikhin International Conference on Learning Representations (ICLR), Workshop Track …, 2018 | 25 | 2018 |
| 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 | 20 | 2019 |
| 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 | 15 | 2023 |
| 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 |
| Wasserstein auto-encoders: Latent dimensionality and random encoders PK Rubenstein, B Schoelkopf, I Tolstikhin International Conference on Learning Representations (ICLR), Workshop Track …, 2018 | 11 | 2018 |
| 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 | 7 | |
| Structural causal models for macro-variables in time-series D Janzing, P Rubenstein, B Schölkopf arXiv preprint arXiv:1804.03911, 2018 | 6 | 2018 |
| 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 | 6 | 2016 |
| On mutual information maximization for representation learning.[arXiv] M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic | 5 | 2020 |
| An empirical study of generative models with encoders PK Rubenstein, Y Li, D Roblek arXiv preprint arXiv:1812.07909, 2018 | 3 | 2018 |
| Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models A Mohtashami, M Verzetti, PK Rubenstein arXiv preprint arXiv:2302.03491, 2023 | 2 | 2023 |
| From deterministic ODEs to dynamic structural causal models. arXiv. org preprint PK Rubenstein, S Bongers, JM Mooij, B Schölkopf arXiv preprint arXiv:1608.08028, 2016 | 2 | 2016 |
| SLM: Bridge the thin gap between speech and text foundation models M Wang, W Han, I Shafran, Z Wu, CC Chiu, Y Cao, Y Wang, N Chen, ... arXiv preprint arXiv:2310.00230, 2023 | 1 | 2023 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
