Paul K Rubenstein
Paul K Rubenstein
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Year
On mutual information maximization for representation learning
M Tschannen, J Djolonga, PK Rubenstein, S Gelly, M Lucic
arXiv preprint arXiv:1907.13625, 2019
642019
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
262017
On the latent space of wasserstein auto-encoders
PK Rubenstein, B Schoelkopf, I Tolstikhin
arXiv preprint arXiv:1802.03761, 2018
172018
Learning Disentangled Representations with Wasserstein Auto-Encoders
PK Rubenstein, B Schölkopf, I Tolstikhin
International Conference on Learning Representations (ICLR), Workshop Track …, 2018
102018
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
102016
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
92019
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
52019
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
42017
Structural causal models for macro-variables in time-series
D Janzing, P Rubenstein, B Schölkopf
arXiv preprint arXiv:1804.03911, 2018
32018
Wasserstein auto-encoders: Latent dimensionality and random encoders
PK Rubenstein, B Schoelkopf, I Tolstikhin
International Conference on Learning Representations (ICLR), Workshop Track …, 2018
32018
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
32016
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
22019
An Empirical Study of Generative Models with Encoders
PK Rubenstein, Y Li, D Roblek
arXiv preprint arXiv:1812.07909, 2018
12018
Three Variable Kernel Independence Testing with Time Series
PK Rubenstein, A Gretton
University College London, 2015
2015
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Articles 1–14