A Kernel Test for Three-Variable Interactions with Random Processes. - Paul K. Rubenstein; Kacper Chwialkowski; Arthur Gretton (UAI, 2016)

Causal Consistency of Structural Equation Models. - Paul K. Rubenstein; Sebastian Weichwald; Stephan Bongers; Joris M. Mooij; Dominik Janzing; Moritz Grosse-Wentrup; Bernhard Schölkopf (UAI, 2017)

Probabilistic Active Learning of Functions in Structural Causal Models. - Paul K. Rubenstein; Ilya O. Tolstikhin; Philipp Hennig; Bernhard Schölkopf (CoRR, 2017)

Learning Disentangled Representations with Wasserstein Auto-Encoders. - Paul K. Rubenstein; Bernhard Schölkopf; Ilya O. Tolstikhin (ICLR (Workshop), 2018)

Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders. - Paul K. Rubenstein; Bernhard Schölkopf; Ilya O. Tolstikhin (ICLR (Workshop), 2018)

From Deterministic ODEs to Dynamic Structural Causal Models. - Paul K. Rubenstein; Stephan Bongers; Joris M. Mooij; Bernhard Schölkopf (UAI, 2018)

On the Latent Space of Wasserstein Auto-Encoders. - Paul K. Rubenstein; Bernhard Schölkopf; Ilya O. Tolstikhin (CoRR, 2018)

An Empirical Study of Generative Models with Encoders. - Paul K. Rubenstein; Yunpeng Li; Dominik Roblek (CoRR, 2018)

Practical and Consistent Estimation of f-Divergences. - Paul K. Rubenstein; Olivier Bousquet; Josip Djolonga; Carlos Riquelme; Ilya O. Tolstikhin (NeurIPS, 2019)

The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA. - Luigi Gresele; Paul K. Rubenstein; Arash Mehrjou; Francesco Locatello; Bernhard Schölkopf (UAI, 2019)

Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks. - Julius von Kügelgen; Paul K. Rubenstein; Bernhard Schölkopf; Adrian Weller (CoRR, 2019)

On Mutual Information Maximization for Representation Learning. - Michael Tschannen; Josip Djolonga; Paul K. Rubenstein; Sylvain Gelly; Mario Lucic (ICLR, 2020)

Advances in latent variable and causal models - Paul K. Rubenstein (2020)

SLM: Bridge the Thin Gap Between Speech and Text Foundation Models. - Mingqiu Wang; Wei Han 002; Izhak Shafran; Zelin Wu; Chung-Cheng Chiu; Yuan Cao 007; Nanxin Chen; Yu Zhang 033; Hagen Soltau; Paul K. Rubenstein; Lukas Zilka; Dian Yu; et al. (ASRU, 2023)

Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations. - Thomas Verelst; Paul K. Rubenstein; Marcin Eichner; Tinne Tuytelaars; Maxim Berman (WACV, 2023)

Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models. - Amirkeivan Mohtashami; Mauro Verzetti; Paul K. Rubenstein (CoRR, 2023)

AudioPaLM: A Large Language Model That Can Speak and Listen. - Paul K. Rubenstein; Chulayuth Asawaroengchai; Duc Dung Nguyen; Ankur Bapna; Zalán Borsos; Félix de Chaumont Quitry; Peter Chen; Dalia El Badawy; Wei Han 002; Eugene Kharitonov; Hannah Muckenhirn; Dirk Padfield; et al. (CoRR, 2023)

SLM: Bridge the thin gap between speech and text foundation models. - Mingqiu Wang; Wei Han 002; Izhak Shafran; Zelin Wu; Chung-Cheng Chiu; Yuan Cao 007; Yongqiang Wang; Nanxin Chen; Yu Zhang 033; Hagen Soltau; Paul K. Rubenstein; Lukas Zilka; et al. (CoRR, 2023)