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Paul K Rubenstein
Paul K Rubenstein
Google DeepMind
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Year
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
7942023
On mutual information maximization for representation learning
M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic
arXiv preprint arXiv:1907.13625, 2019
5172019
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
1102017
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
812023
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
792019
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ...
arXiv preprint arXiv:2403.05530, 2024
692024
On the latent space of wasserstein auto-encoders
PK Rubenstein, B Schoelkopf, I Tolstikhin
arXiv preprint arXiv:1802.03761, 2018
552018
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
482016
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
472019
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
272019
Learning Disentangled Representations with Wasserstein Auto-Encoders
PK Rubenstein, B Schölkopf, I Tolstikhin
International Conference on Learning Representations (ICLR), Workshop Track …, 2018
262018
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
212023
Wasserstein auto-encoders: Latent dimensionality and random encoders
PK Rubenstein, B Schoelkopf, I Tolstikhin
International Conference on Learning Representations (ICLR), Workshop Track …, 2018
122018
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
122017
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
112023
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
10
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
82016
On mutual information maximization for representation learning.[arXiv]
M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic
arXiv preprint arXiv:1907.13625, 2019
62019
Structural causal models for macro-variables in time-series
D Janzing, P Rubenstein, B Schölkopf
arXiv preprint arXiv:1804.03911, 2018
62018
Learning translation quality evaluation on low resource languages from large language models
A Mohtashami, M Verzetti, PK Rubenstein
arXiv preprint arXiv:2302.03491, 2023
52023
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