Arthur Gretton

ORCID

Publication Activity (10 Years)

Years Active: 2002-2024
Publications (10 Years): 136

Top Venues

Publications

Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland
A Distributional Analogue to the Successor Representation. CoRR (2024)
Tongzheng Ren, Haotian Sun, Antoine Moulin, Arthur Gretton, Bo Dai
Spectral Representation for Causal Estimation with Hidden Confounders. CoRR (2024)
Alexandre Galashov, Valentin De Bortoli, Arthur Gretton
Deep MMD Gradient Flow without adversarial training. CoRR (2024)
Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton
Proxy Methods for Domain Adaptation. AISTATS (2024)
Zonghao Chen, Masha Naslidnyk, Arthur Gretton, François-Xavier Briol
Conditional Bayesian Quadrature. CoRR (2024)
Roman Pogodin, Antonin Schrab, Yazhe Li, Danica J. Sutherland, Arthur Gretton
Practical Kernel Tests of Conditional Independence. CoRR (2024)
Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa
Mind the Graph When Balancing Data for Fairness or Robustness. CoRR (2024)
Dimitri Meunier, Zikai Shen, Mattes Mollenhauer, Arthur Gretton, Zhu Li
Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms. CoRR (2024)
Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton
Proxy Methods for Domain Adaptation. CoRR (2024)
Liyuan Xu, Arthur Gretton
A Neural Mean Embedding Approach for Back-door and Front-door Adjustment. ICLR (2023)
Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm. CoRR (2023)
Jerome Baum, Heishiro Kanagawa, Arthur Gretton
A Kernel Stein Test of Goodness of Fit for Sequential Models. ICML (2023)
Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai
Adapting to Latent Subgroup Shifts via Concepts and Proxies. AISTATS (2023)
Lisa M. Koch, Christian M. Schürch, Christian F. Baumgartner, Arthur Gretton, Philipp Berens
Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images. CoRR (2023)
Li Kevin Wenliang, Grégoire Delétang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland
Distributional Bellman Operators over Mean Embeddings. CoRR (2023)
William I. Walker, Arthur Gretton, Maneesh Sahani
Prediction under Latent Subgroup Shifts with High-Dimensional Observations. CoRR (2023)
Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton
MMD Aggregated Two-Sample Test. J. Mach. Learn. Res. 24 (2023)
Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton
Fast and scalable score-based kernel calibration tests. UAI (2023)
Liyuan Xu, Arthur Gretton
Kernel Single Proxy Control for Deterministic Confounding. CoRR (2023)
Felix Biggs, Antonin Schrab, Arthur Gretton
MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting. NeurIPS (2023)
Dimitri Meunier, Zhu Li, Arthur Gretton, Samory Kpotufe
Nonlinear Meta-Learning Can Guarantee Faster Rates. CoRR (2023)
Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
Efficient Conditionally Invariant Representation Learning. ICLR (2023)
Felix Biggs, Antonin Schrab, Arthur Gretton
MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting. CoRR (2023)
Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet
On Instrumental Variable Regression for Deep Offline Policy Evaluation. J. Mach. Learn. Res. 23 (2022)
Antonin Schrab, Ilmun Kim, Benjamin Guedj, Arthur Gretton
Efficient Aggregated Kernel Tests using Incomplete U-statistics. CoRR (2022)
Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
Efficient Conditionally Invariant Representation Learning. CoRR (2022)
Heishiro Kanagawa, Arthur Gretton, Lester Mackey
Controlling Moments with Kernel Stein Discrepancies. CoRR (2022)
Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton
Importance Weighting Approach in Kernel Bayes' Rule. CoRR (2022)
Jerome Baum, Heishiro Kanagawa, Arthur Gretton
A kernel Stein test of goodness of fit for sequential models. CoRR (2022)
Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton
Importance Weighted Kernel Bayes' Rule. ICML (2022)
Chieh Tzu Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy
Deep Layer-wise Networks Have Closed-Form Weights. AISTATS (2022)
Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai
Adapting to Latent Subgroup Shifts via Concepts and Proxies. CoRR (2022)
Antonin Schrab, Benjamin Guedj, Arthur Gretton
KSD Aggregated Goodness-of-fit Test. NeurIPS (2022)
Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach. CoRR (2022)
Liyuan Xu, Arthur Gretton
A Neural Mean Embedding Approach for Back-door and Front-door Adjustment. CoRR (2022)
Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton
Optimal Rates for Regularized Conditional Mean Embedding Learning. CoRR (2022)
Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva
Causal inference with treatment measurement error: a nonparametric instrumental variable approach. UAI (2022)
Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy
Deep Layer-wise Networks Have Closed-Form Weights. CoRR (2022)
Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton
Optimal Rates for Regularized Conditional Mean Embedding Learning. NeurIPS (2022)
Antonin Schrab, Wittawat Jitkrittum, Zoltán Szabó, Dino Sejdinovic, Arthur Gretton
Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'. CoRR (2022)
Antonin Schrab, Ilmun Kim, Benjamin Guedj, Arthur Gretton
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics. NeurIPS (2022)
Antonin Schrab, Benjamin Guedj, Arthur Gretton
KSD Aggregated Goodness-of-fit Test. CoRR (2022)
Lisa M. Koch, Christian M. Schürch, Arthur Gretton, Philipp Berens
Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection. MIDL (2022)
Pierre Glaser, Michael Arbel, Arnaud Doucet, Arthur Gretton
Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference. CoRR (2022)
Pierre Glaser, Michael Arbel, Arthur Gretton
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support. CoRR (2021)
Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet
On Instrumental Variable Regression for Deep Offline Policy Evaluation. CoRR (2021)
Liyuan Xu, Heishiro Kanagawa, Arthur Gretton
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation. CoRR (2021)
Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton
MMD Aggregated Two-Sample Test. CoRR (2021)
Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. ICML (2021)
Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton
Self-Supervised Learning with Kernel Dependence Maximization. NeurIPS (2021)
Alexander Marx, Arthur Gretton, Joris M. Mooij
A weaker faithfulness assumption based on triple interactions. UAI (2021)
Pierre Glaser, Michael Arbel, Arthur Gretton
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support. NeurIPS (2021)
Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. CoRR (2021)
Rahul Singh, Liyuan Xu, Arthur Gretton
Kernel Methods for Multistage Causal Inference: Mediation Analysis and Dynamic Treatment Effects. CoRR (2021)
Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton
Self-Supervised Learning with Kernel Dependence Maximization. CoRR (2021)
Liyuan Xu, Heishiro Kanagawa, Arthur Gretton
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation. NeurIPS (2021)
Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton
Learning Deep Features in Instrumental Variable Regression. ICLR (2021)
Oscar Key, Tamara Fernandez, Arthur Gretton, François-Xavier Briol
Composite Goodness-of-fit Tests with Kernels. CoRR (2021)
Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton
Efficient Wasserstein Natural Gradients for Reinforcement Learning. ICLR (2021)
Michael Arbel, Liang Zhou, Arthur Gretton
Generalized Energy Based Models. ICLR (2021)
Zhu Li, Zhi-Hua Zhou, Arthur Gretton
Towards an Understanding of Benign Overfitting in Neural Networks. CoRR (2021)
Alexander Marx, Arthur Gretton, Joris M. Mooij
A Weaker Faithfulness Assumption based on Triple Interactions. CoRR (2020)
Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton
A Non-Asymptotic Analysis for Stein Variational Gradient Descent. CoRR (2020)
Rahul Singh, Liyuan Xu, Arthur Gretton
Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and Off-Policy Planning. CoRR (2020)
Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland
Learning Deep Kernels for Non-Parametric Two-Sample Tests. ICML (2020)
Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland
Learning Deep Kernels for Non-Parametric Two-Sample Tests. CoRR (2020)
Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre
Conditional BRUNO: A neural process for exchangeable labelled data. Neurocomputing 416 (2020)
Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy
Kernel Dependence Network. CoRR (2020)
Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton
Learning Deep Features in Instrumental Variable Regression. CoRR (2020)
Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy
Layer-wise Learning of Kernel Dependence Networks. CoRR (2020)
Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu
Model-based kernel sum rule: kernel Bayesian inference with probabilistic models. Mach. Learn. 109 (5) (2020)
Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton
Efficient Wasserstein Natural Gradients for Reinforcement Learning. CoRR (2020)
Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton
A Non-Asymptotic Analysis for Stein Variational Gradient Descent. NeurIPS (2020)
Tamara Fernandez, Wenkai Xu, Marc Ditzhaus, Arthur Gretton
A kernel test for quasi-independence. NeurIPS (2020)
Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed
A case for new neural network smoothness constraints. CoRR (2020)
Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data. ICML (2020)
Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed
A case for new neural network smoothness constraints. ICBINB@NeurIPS (2020)
Michael Arbel, Liang Zhou, Arthur Gretton
KALE: When Energy-Based Learning Meets Adversarial Training. CoRR (2020)
Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar
Kernelized Wasserstein Natural Gradient. ICLR (2020)
Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data. CoRR (2020)
Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton
Maximum Mean Discrepancy Gradient Flow. NeurIPS (2019)
Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton
Maximum Mean Discrepancy Gradient Flow. CoRR (2019)
Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani
Antithetic and Monte Carlo kernel estimators for partial rankings. Stat. Comput. 29 (5) (2019)
Tamara Fernandez, Arthur Gretton
A maximum-mean-discrepancy goodness-of-fit test for censored data. AISTATS (2019)
Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar
Kernelized Wasserstein Natural Gradient. CoRR (2019)
Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He
Kernel Exponential Family Estimation via Doubly Dual Embedding. AISTATS (2019)
Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton
Learning deep kernels for exponential family densities. ICML (2019)
Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre
Conditional BRUNO: a neural process for exchangeable labelled data. ESANN (2019)
Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans
Exponential Family Estimation via Adversarial Dynamics Embedding. CoRR (2019)
Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton
A Kernel Stein Test for Comparing Latent Variable Models. CoRR (2019)
Rahul Singh, Maneesh Sahani, Arthur Gretton
Kernel Instrumental Variable Regression. CoRR (2019)
Rahul Singh, Maneesh Sahani, Arthur Gretton
Kernel Instrumental Variable Regression. NeurIPS (2019)
Nicolò Colombo, Ricardo Silva, Soong Moon Kang, Arthur Gretton
Counterfactual Distribution Regression for Structured Inference. CoRR (2019)
Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans
Exponential Family Estimation via Adversarial Dynamics Embedding. NeurIPS (2019)
Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton
Demystifying MMD GANs. ICLR (Poster) (2018)
Qinyi Zhang, Sarah Filippi, Arthur Gretton, Dino Sejdinovic
Large-scale kernel methods for independence testing. Stat. Comput. 28 (1) (2018)
Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre
BRUNO: A Deep Recurrent Model for Exchangeable Data. NeurIPS (2018)
Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton
Informative Features for Model Comparison. CoRR (2018)
Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton
Efficient and principled score estimation with Nyström kernel exponential families. AISTATS (2018)
Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton
Demystifying MMD GANs. CoRR (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Arthur Gretton