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A case for new neural network smoothness constraints


Dec 21, 2020
Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed


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A kernel test for quasi-independence


Nov 17, 2020
Tamara Fernández, Wenkai Xu, Marc Ditzhaus, Arthur Gretton


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Kernel Dependence Network


Nov 09, 2020
Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer Dy

* NeurIPS2020 Workshop (Beyond Backprop) 
* arXiv admin note: substantial text overlap with arXiv:2006.08539 

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Learning Deep Features in Instrumental Variable Regression


Nov 01, 2020
Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton


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A Weaker Faithfulness Assumption based on Triple Interactions


Oct 27, 2020
Alexander Marx, Arthur Gretton, Joris M. Mooij


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Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and Off-Policy Planning


Oct 13, 2020
Rahul Singh, Liyuan Xu, Arthur Gretton

* 66 pages, 6 figures 

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Efficient Wasserstein Natural Gradients for Reinforcement Learning


Oct 12, 2020
Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton


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Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data


Aug 26, 2020
Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton

* Proceedings of the International Conference on Machine Learning, 2020 

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A Non-Asymptotic Analysis for Stein Variational Gradient Descent


Jun 17, 2020
Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton


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Layer-wise Learning of Kernel Dependence Networks


Jun 15, 2020
Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer Dy


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KALE: When Energy-Based Learning Meets Adversarial Training


Mar 10, 2020
Michael Arbel, Liang Zhou, Arthur Gretton


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Learning Deep Kernels for Non-Parametric Two-Sample Tests


Feb 21, 2020
Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, D. J. Sutherland


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A kernel log-rank test of independence for right-censored data


Dec 08, 2019
Tamara Fernandez, Arthur Gretton, David Rindt, Dino Sejdinovic


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Kernelized Wasserstein Natural Gradient


Oct 25, 2019
Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montufar


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Counterfactual Distribution Regression for Structured Inference


Aug 20, 2019
Nicolo Colombo, Ricardo Silva, Soong M Kang, Arthur Gretton

* 24 pages, 5 figures 

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A Kernel Stein Test for Comparing Latent Variable Models


Jul 01, 2019
Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton


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Maximum Mean Discrepancy Gradient Flow


Jun 11, 2019
Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton


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Kernel Instrumental Variable Regression


Jun 01, 2019
Rahul Singh, Maneesh Sahani, Arthur Gretton

* 31 pages, 8 figures 

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Exponential Family Estimation via Adversarial Dynamics Embedding


Apr 27, 2019
Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans

* 66 figures, 25 pages; preliminary version published in NeurIPS2018 Bayesian Deep Learning Workshop 

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Learning deep kernels for exponential family densities


Nov 22, 2018
Li Wenliang, Dougal Sutherland, Heiko Strathmann, Arthur Gretton


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Kernel Exponential Family Estimation via Doubly Dual Embedding


Nov 06, 2018
Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He

* 22 pages, 20 figures 

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On gradient regularizers for MMD GANs


Oct 27, 2018
Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, Arthur Gretton

* Code available at https://github.com/MichaelArbel/Scaled-MMD-GAN . v2: NIPS camera-ready version 

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Informative Features for Model Comparison


Oct 27, 2018
Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton

* Accepted to NIPS 2018 

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BRUNO: A Deep Recurrent Model for Exchangeable Data


Oct 16, 2018
Iryna Korshunova, Jonas Degrave, Ferenc Huszár, Yarin Gal, Arthur Gretton, Joni Dambre

* NIPS 2018 

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Antithetic and Monte Carlo kernel estimators for partial rankings


Jul 25, 2018
Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani


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Kernel Conditional Exponential Family


Apr 08, 2018
Michael Arbel, Arthur Gretton


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