Paul K. Rubenstein

According to our database1, Paul K. Rubenstein authored at least 18 papers between 2016 and 2023.

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Bibliography

2023
SLM: Bridge the thin gap between speech and text foundation models.
CoRR, 2023

AudioPaLM: A Large Language Model That Can Speak and Listen.
CoRR, 2023

Learning Translation Quality Evaluation on Low Resource Languages from Large Language Models.
CoRR, 2023

Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

SLM: Bridge the Thin Gap Between Speech and Text Foundation Models.
Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop, 2023

2020
Advances in latent variable and causal models
PhD thesis, 2020

On Mutual Information Maximization for Representation Learning.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks.
CoRR, 2019

The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Practical and Consistent Estimation of f-Divergences.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
An Empirical Study of Generative Models with Encoders.
CoRR, 2018

On the Latent Space of Wasserstein Auto-Encoders.
CoRR, 2018

From Deterministic ODEs to Dynamic Structural Causal Models.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Wasserstein Auto-Encoders: Latent Dimensionality and Random Encoders.
Proceedings of the 6th International Conference on Learning Representations, 2018

Learning Disentangled Representations with Wasserstein Auto-Encoders.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Probabilistic Active Learning of Functions in Structural Causal Models.
CoRR, 2017

Causal Consistency of Structural Equation Models.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

2016
A Kernel Test for Three-Variable Interactions with Random Processes.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016


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