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Arthur Gretton

KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support


Jun 16, 2021
Pierre Glaser, Michael Arbel, Arthur Gretton

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Self-Supervised Learning with Kernel Dependence Maximization


Jun 15, 2021
Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton

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Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation


Jun 07, 2021
Liyuan Xu, Heishiro Kanagawa, Arthur Gretton

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Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction


Jun 06, 2021
Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet

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Towards an Understanding of Benign Overfitting in Neural Networks


Jun 06, 2021
Zhu Li, Zhi-Hua Zhou, Arthur Gretton


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On Instrumental Variable Regression for Deep Offline Policy Evaluation


May 21, 2021
Yutian Chen, Liyuan Xu, Caglar Gulcehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet

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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

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* 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

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* 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

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* 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

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* 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

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* 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

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