Arthur Gretton

274 papers A* 55A 28B 6Misc 1Journal 144Unranked 40

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Efficient Inference after Directionally Stable Adaptive Experiments. CoRR Zikai Shen, Houssam Zenati, Nathan Kallus, Arthur Gretton, Koulik Khamaru, Aurélien Bibaut
2026	J	`jnl`	Regularized f-Divergence Kernel Tests. CoRR Mónica Ribero, Antonin Schrab, Arthur Gretton
2025	J	`jnl`	(De)-regularized Maximum Mean Discrepancy Gradient Flow. J. Mach. Learn. Res. Zonghao Chen, Aratrika Mustafi, Pierre Glaser, Anna Korba, Arthur Gretton, Bharath K. Sriperumbudur
2025	A	`conf`	A Unified Data Representation Learning for Non-parametric Two-sample Testing. UAI Xunye Tian, Liuhua Peng, Zhijian Zhou, Mingming Gong, Arthur Gretton, Feng Liu
2025	A*	`conf`	Accelerated Diffusion Models via Speculative Sampling. ICML Valentin De Bortoli, Alexandre Galashov, Arthur Gretton, Arnaud Doucet
2025	J	`jnl`	Accelerated Diffusion Models via Speculative Sampling. CoRR Valentin De Bortoli, Alexandre Galashov, Arthur Gretton, Arnaud Doucet
2025	J	`jnl`	Closed-Form Last Layer Optimization. CoRR Alexandre Galashov, Nathaël Da Costa, Liyuan Xu, Philipp Hennig, Arthur Gretton
2025	J	`jnl`	Composite Goodness-of-fit Tests with Kernels. J. Mach. Learn. Res. Oscar Key, Arthur Gretton, François-Xavier Briol, Tamara Fernandez
2025	A	`conf`	Credal Two-Sample Tests of Epistemic Uncertainty. AISTATS Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet
2025	A*	`conf`	Deep MMD Gradient Flow without adversarial training. ICLR Alexandre Galashov, Valentin De Bortoli, Arthur Gretton
2025	J	`jnl`	Demystifying Spectral Feature Learning for Instrumental Variable Regression. CoRR Dimitri Meunier, Antoine Moulin, Jakub Wornbard, Vladimir R. Kostic, Arthur Gretton
2025	J	`jnl`	Density Ratio-Free Doubly Robust Proxy Causal Learning. CoRR Bariscan Bozkurt, Houssam Zenati, Dimitri Meunier, Liyuan Xu, Arthur Gretton
2025	A	`conf`	Density Ratio-based Proxy Causal Learning Without Density Ratios. AISTATS Bariscan Bozkurt, Ben Deaner, Dimitri Meunier, Liyuan Xu, Arthur Gretton
2025	J	`jnl`	Density Ratio-based Proxy Causal Learning Without Density Ratios. CoRR Bariscan Bozkurt, Ben Deaner, Dimitri Meunier, Liyuan Xu, Arthur Gretton
2025	A*	`conf`	Distributional Diffusion Models with Scoring Rules. ICML Valentin De Bortoli, Alexandre Galashov, J. Swaroop Guntupalli, Guangyao Zhou, Kevin Patrick Murphy, Arthur Gretton, Arnaud Doucet
2025	J	`jnl`	Distributional Diffusion Models with Scoring Rules. CoRR Valentin De Bortoli, Alexandre Galashov, J. Swaroop Guntupalli, Guangyao Zhou, Kevin Murphy, Arthur Gretton, Arnaud Doucet
2025	J	`jnl`	Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings. CoRR Houssam Zenati, Bariscan Bozkurt, Arthur Gretton
2025	J	`jnl`	Fast and Scalable Score-Based Kernel Calibration Tests. CoRR Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton
2025	A	`conf`	Kernel Single Proxy Control for Deterministic Confounding. AISTATS Liyuan Xu, Arthur Gretton
2025	J	`jnl`	Kernel Treatment Effects with Adaptively Collected Data. CoRR Houssam Zenati, Bariscan Bozkurt, Arthur Gretton
2025	J	`jnl`	Learn to Guide Your Diffusion Model. CoRR Alexandre Galashov, Ashwini Pokle, Arnaud Doucet, Arthur Gretton, Mauricio Delbracio, Valentin De Bortoli
2025	A*	`conf`	Learning-Order Autoregressive Models with Application to Molecular Graph Generation. ICML Zhe Wang, Jiaxin Shi, Nicolas Heess, Arthur Gretton, Michalis K. Titsias
2025	J	`jnl`	Learning-Order Autoregressive Models with Application to Molecular Graph Generation. CoRR Zhe Wang, Jiaxin Shi, Nicolas Heess, Arthur Gretton, Michalis K. Titsias
2025	J	`jnl`	Near-Optimality of Contrastive Divergence Algorithms. CoRR Pierre Glaser, Kevin Han Huang, Arthur Gretton
2025	J	`jnl`	Nonlinear Meta-learning Can Guarantee Faster Rates. SIAM J. Math. Data Sci. Dimitri Meunier, Zhu Li, Arthur Gretton, Samory Kpotufe
2025	J	`jnl`	Nonparametric Instrumental Variable Regression with Observed Covariates. CoRR Zikai Shen, Zonghao Chen, Dimitri Meunier, Ingo Steinwart, Arthur Gretton, Zhu Li
2025	J	`jnl`	On the Hardness of Conditional Independence Testing In Practice. CoRR Zheng He, Roman Pogodin, Yazhe Li, Namrata Deka, Arthur Gretton, Danica J. Sutherland
2025	A*	`conf`	Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression. ICLR Juno Kim, Dimitri Meunier, Arthur Gretton, Taiji Suzuki, Zhu Li
2025	J	`jnl`	Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression. CoRR Juno Kim, Dimitri Meunier, Arthur Gretton, Taiji Suzuki, Zhu Li
2025	J	`jnl`	Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression. CoRR Dimitri Meunier, Jakub Wornbard, Vladimir R. Kostic, Antoine Moulin, Alek Fröhlich, Karim Lounici, Massimiliano Pontil, Arthur Gretton
2025	J	`jnl`	Regularized least squares learning with heavy-tailed noise is minimax optimal. CoRR Mattes Mollenhauer, Nicole Mücke, Dimitri Meunier, Arthur Gretton
2025	—	`conf`	Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions. ACL (Findings) Taedong Yun, Eric Yang, Mustafa Safdari, Jong Ha Lee, Vaishnavi Vinod Kumar, S. Sara Mahdavi, Jonathan Amar, Derek Peyton, Reut Aharony, Andreas Michaelides, Logan Douglas Schneider, Isaac R. Galatzer-Levy, Yugang Jia, John Canny, Arthur Gretton, Maja J. Mataric
2025	J	`jnl`	Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions. CoRR Taedong Yun, Eric Yang, Mustafa Safdari, Jong Ha Lee, Vaishnavi Vinod Kumar, S. Sara Mahdavi, Jonathan Amar, Derek Peyton, Reut Aharony, Andreas Michaelides, Logan D. Schneider, Isaac R. Galatzer-Levy, Yugang Jia, John Canny, Arthur Gretton, Maja J. Mataric
2025	A	`conf`	Spectral Representation for Causal Estimation with Hidden Confounders. AISTATS Haotian Sun, Antoine Moulin, Tongzheng Ren, Arthur Gretton, Bo Dai
2025	J	`jnl`	Towards a Unified Analysis of Neural Networks in Nonparametric Instrumental Variable Regression: Optimization and Generalization. CoRR Zonghao Chen, Atsushi Nitanda, Arthur Gretton, Taiji Suzuki
2024	J	`jnl`	(De)-regularized Maximum Mean Discrepancy Gradient Flow. CoRR Zonghao Chen, Aratrika Mustafi, Pierre Glaser, Anna Korba, Arthur Gretton, Bharath K. Sriperumbudur
2024	A*	`conf`	A Distributional Analogue to the Successor Representation. ICML Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland
2024	J	`jnl`	A Distributional Analogue to the Successor Representation. CoRR Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland
2024	A	`conf`	Conditional Bayesian Quadrature. UAI Zonghao Chen, Masha Naslidnyk, Arthur Gretton, François-Xavier Briol
2024	J	`jnl`	Conditional Bayesian Quadrature. CoRR Zonghao Chen, Masha Naslidnyk, Arthur Gretton, François-Xavier Briol
2024	J	`jnl`	Credal Two-Sample Tests of Epistemic Ignorance. CoRR Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet
2024	J	`jnl`	Deep MMD Gradient Flow without adversarial training. CoRR Alexandre Galashov, Valentin De Bortoli, Arthur Gretton
2024	A*	`conf`	Distributional Bellman Operators over Mean Embeddings. ICML Li Kevin Wenliang, Grégoire Delétang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland
2024	A*	`conf`	Foundations of Multivariate Distributional Reinforcement Learning. NeurIPS Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Mark Rowland
2024	J	`jnl`	Foundations of Multivariate Distributional Reinforcement Learning. CoRR Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Mark Rowland
2024	A*	`conf`	Mind the Graph When Balancing Data for Fairness or Robustness. NeurIPS Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa
2024	J	`jnl`	Mind the Graph When Balancing Data for Fairness or Robustness. CoRR Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa
2024	A*	`conf`	Near-Optimality of Contrastive Divergence Algorithms. NeurIPS Pierre Glaser, Kevin Han Huang, Arthur Gretton
2024	J	`jnl`	Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal. CoRR Dimitri Meunier, Zhu Li, Tim Christensen, Arthur Gretton
2024	A*	`conf`	Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms. NeurIPS Dimitri Meunier, Zikai Shen, Mattes Mollenhauer, Arthur Gretton, Zhu Li
2024	J	`jnl`	Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms. CoRR Dimitri Meunier, Zikai Shen, Mattes Mollenhauer, Arthur Gretton, Zhu Li
2024	J	`jnl`	Practical Kernel Tests of Conditional Independence. CoRR Roman Pogodin, Antonin Schrab, Yazhe Li, Danica J. Sutherland, Arthur Gretton
2024	J	`jnl`	Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation. CoRR Eleni Sgouritsa, Virginia Aglietti, Yee Whye Teh, Arnaud Doucet, Arthur Gretton, Silvia Chiappa
2024	A	`conf`	Proxy Methods for Domain Adaptation. AISTATS Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton
2024	J	`jnl`	Proxy Methods for Domain Adaptation. CoRR Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton
2024	J	`jnl`	Spectral Representation for Causal Estimation with Hidden Confounders. CoRR Tongzheng Ren, Haotian Sun, Antoine Moulin, Arthur Gretton, Bo Dai
2024	J	`jnl`	Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian Processes. CoRR Hugh Dance, Peter Orbanz, Arthur Gretton
2024	J	`jnl`	Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm. J. Mach. Learn. Res. Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton
2023	A*	`conf`	A Kernel Stein Test of Goodness of Fit for Sequential Models. ICML Jerome Baum, Heishiro Kanagawa, Arthur Gretton
2023	A*	`conf`	A Neural Mean Embedding Approach for Back-door and Front-door Adjustment. ICLR Liyuan Xu, Arthur Gretton
2023	A	`conf`	Adapting to Latent Subgroup Shifts via Concepts and Proxies. AISTATS Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai
2023	J	`jnl`	Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images. CoRR Lisa M. Koch, Christian M. Schürch, Christian F. Baumgartner, Arthur Gretton, Philipp Berens
2023	J	`jnl`	Distributional Bellman Operators over Mean Embeddings. CoRR Li Kevin Wenliang, Grégoire Delétang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland
2023	A*	`conf`	Efficient Conditionally Invariant Representation Learning. ICLR Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
2023	A	`conf`	Fast and scalable score-based kernel calibration tests. UAI Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton
2023	J	`jnl`	Kernel Single Proxy Control for Deterministic Confounding. CoRR Liyuan Xu, Arthur Gretton
2023	J	`jnl`	MMD Aggregated Two-Sample Test. J. Mach. Learn. Res. Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton
2023	J	`jnl`	MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting. CoRR Felix Biggs, Antonin Schrab, Arthur Gretton
2023	A*	`conf`	MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting. NeurIPS Felix Biggs, Antonin Schrab, Arthur Gretton
2023	J	`jnl`	Nonlinear Meta-Learning Can Guarantee Faster Rates. CoRR Dimitri Meunier, Zhu Li, Arthur Gretton, Samory Kpotufe
2023	J	`jnl`	Prediction under Latent Subgroup Shifts with High-Dimensional Observations. CoRR William I. Walker, Arthur Gretton, Maneesh Sahani
2023	J	`jnl`	Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm. CoRR Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton
2022	J	`jnl`	A Neural Mean Embedding Approach for Back-door and Front-door Adjustment. CoRR Liyuan Xu, Arthur Gretton
2022	J	`jnl`	A kernel Stein test of goodness of fit for sequential models. CoRR Jerome Baum, Heishiro Kanagawa, Arthur Gretton
2022	J	`jnl`	Adapting to Latent Subgroup Shifts via Concepts and Proxies. CoRR Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai
2022	J	`jnl`	Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach. CoRR Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva
2022	A	`conf`	Causal inference with treatment measurement error: a nonparametric instrumental variable approach. UAI Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva
2022	J	`jnl`	Controlling Moments with Kernel Stein Discrepancies. CoRR Heishiro Kanagawa, Arthur Gretton, Lester Mackey
2022	A	`conf`	Deep Layer-wise Networks Have Closed-Form Weights. AISTATS Chieh Tzu Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy
2022	J	`jnl`	Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'. CoRR Antonin Schrab, Wittawat Jitkrittum, Zoltán Szabó, Dino Sejdinovic, Arthur Gretton
2022	A*	`conf`	Efficient Aggregated Kernel Tests using Incomplete $U$-statistics. NeurIPS Antonin Schrab, Ilmun Kim, Benjamin Guedj, Arthur Gretton
2022	J	`jnl`	Efficient Aggregated Kernel Tests using Incomplete U-statistics. CoRR Antonin Schrab, Ilmun Kim, Benjamin Guedj, Arthur Gretton
2022	J	`jnl`	Efficient Conditionally Invariant Representation Learning. CoRR Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton
2022	—	`conf`	Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection. MIDL Lisa M. Koch, Christian M. Schürch, Arthur Gretton, Philipp Berens
2022	A*	`conf`	Importance Weighted Kernel Bayes' Rule. ICML Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton
2022	J	`jnl`	Importance Weighting Approach in Kernel Bayes' Rule. CoRR Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton
2022	A*	`conf`	KSD Aggregated Goodness-of-fit Test. NeurIPS Antonin Schrab, Benjamin Guedj, Arthur Gretton
2022	J	`jnl`	KSD Aggregated Goodness-of-fit Test. CoRR Antonin Schrab, Benjamin Guedj, Arthur Gretton
2022	J	`jnl`	Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference. CoRR Pierre Glaser, Michael Arbel, Arnaud Doucet, Arthur Gretton
2022	J	`jnl`	On Instrumental Variable Regression for Deep Offline Policy Evaluation. J. Mach. Learn. Res. Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet
2022	A*	`conf`	Optimal Rates for Regularized Conditional Mean Embedding Learning. NeurIPS Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton
2022	J	`jnl`	Optimal Rates for Regularized Conditional Mean Embedding Learning. CoRR Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton
2021	A	`conf`	A weaker faithfulness assumption based on triple interactions. UAI Alexander Marx, Arthur Gretton, Joris M. Mooij
2021	J	`jnl`	Composite Goodness-of-fit Tests with Kernels. CoRR Oscar Key, Tamara Fernandez, Arthur Gretton, François-Xavier Briol
2021	A*	`conf`	Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation. NeurIPS Liyuan Xu, Heishiro Kanagawa, Arthur Gretton
2021	J	`jnl`	Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation. CoRR Liyuan Xu, Heishiro Kanagawa, Arthur Gretton
2021	A*	`conf`	Efficient Wasserstein Natural Gradients for Reinforcement Learning. ICLR Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton
2021	A*	`conf`	Generalized Energy Based Models. ICLR Michael Arbel, Liang Zhou, Arthur Gretton
2021	A*	`conf`	KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support. NeurIPS Pierre Glaser, Michael Arbel, Arthur Gretton
2021	J	`jnl`	KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support. CoRR Pierre Glaser, Michael Arbel, Arthur Gretton
2021	J	`jnl`	Kernel Methods for Multistage Causal Inference: Mediation Analysis and Dynamic Treatment Effects. CoRR Rahul Singh, Liyuan Xu, Arthur Gretton
2021	A*	`conf`	Learning Deep Features in Instrumental Variable Regression. ICLR Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton
2021	J	`jnl`	MMD Aggregated Two-Sample Test. CoRR Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton
2021	J	`jnl`	On Instrumental Variable Regression for Deep Offline Policy Evaluation. CoRR Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet
2021	A*	`conf`	Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. ICML Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet
2021	J	`jnl`	Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. CoRR Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet
2021	A*	`conf`	Self-Supervised Learning with Kernel Dependence Maximization. NeurIPS Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton
2021	J	`jnl`	Self-Supervised Learning with Kernel Dependence Maximization. CoRR Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton
2021	J	`jnl`	Towards an Understanding of Benign Overfitting in Neural Networks. CoRR Zhu Li, Zhi-Hua Zhou, Arthur Gretton
2020	A*	`conf`	A Non-Asymptotic Analysis for Stein Variational Gradient Descent. NeurIPS Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton
2020	J	`jnl`	A Non-Asymptotic Analysis for Stein Variational Gradient Descent. CoRR Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton
2020	J	`jnl`	A Weaker Faithfulness Assumption based on Triple Interactions. CoRR Alexander Marx, Arthur Gretton, Joris M. Mooij
2020	—	`conf`	A case for new neural network smoothness constraints. ICBINB@NeurIPS Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed
2020	J	`jnl`	A case for new neural network smoothness constraints. CoRR Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed
2020	A*	`conf`	A kernel test for quasi-independence. NeurIPS Tamara Fernandez, Wenkai Xu, Marc Ditzhaus, Arthur Gretton
2020	J	`jnl`	Conditional BRUNO: A neural process for exchangeable labelled data. Neurocomputing Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre
2020	J	`jnl`	Efficient Wasserstein Natural Gradients for Reinforcement Learning. CoRR Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton
2020	J	`jnl`	KALE: When Energy-Based Learning Meets Adversarial Training. CoRR Michael Arbel, Liang Zhou, Arthur Gretton
2020	J	`jnl`	Kernel Dependence Network. CoRR Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy
2020	J	`jnl`	Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and Off-Policy Planning. CoRR Rahul Singh, Liyuan Xu, Arthur Gretton
2020	A*	`conf`	Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data. ICML Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton
2020	J	`jnl`	Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data. CoRR Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton
2020	A*	`conf`	Kernelized Wasserstein Natural Gradient. ICLR Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar
2020	J	`jnl`	Layer-wise Learning of Kernel Dependence Networks. CoRR Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy
2020	J	`jnl`	Learning Deep Features in Instrumental Variable Regression. CoRR Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton
2020	A*	`conf`	Learning Deep Kernels for Non-Parametric Two-Sample Tests. ICML Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland
2020	J	`jnl`	Learning Deep Kernels for Non-Parametric Two-Sample Tests. CoRR Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland
2020	J	`jnl`	Model-based kernel sum rule: kernel Bayesian inference with probabilistic models. Mach. Learn. Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu
2019	J	`jnl`	A Kernel Stein Test for Comparing Latent Variable Models. CoRR Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton
2019	A	`conf`	A maximum-mean-discrepancy goodness-of-fit test for censored data. AISTATS Tamara Fernandez, Arthur Gretton
2019	J	`jnl`	Antithetic and Monte Carlo kernel estimators for partial rankings. Stat. Comput. Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani
2019	B	`conf`	Conditional BRUNO: a neural process for exchangeable labelled data. ESANN Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre
2019	J	`jnl`	Counterfactual Distribution Regression for Structured Inference. CoRR Nicolò Colombo, Ricardo Silva, Soong Moon Kang, Arthur Gretton
2019	A*	`conf`	Exponential Family Estimation via Adversarial Dynamics Embedding. NeurIPS Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans
2019	J	`jnl`	Exponential Family Estimation via Adversarial Dynamics Embedding. CoRR Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans
2019	A	`conf`	Kernel Exponential Family Estimation via Doubly Dual Embedding. AISTATS Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He
2019	A*	`conf`	Kernel Instrumental Variable Regression. NeurIPS Rahul Singh, Maneesh Sahani, Arthur Gretton
2019	J	`jnl`	Kernel Instrumental Variable Regression. CoRR Rahul Singh, Maneesh Sahani, Arthur Gretton
2019	J	`jnl`	Kernelized Wasserstein Natural Gradient. CoRR Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar
2019	A*	`conf`	Learning deep kernels for exponential family densities. ICML Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton
2019	A*	`conf`	Maximum Mean Discrepancy Gradient Flow. NeurIPS Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton
2019	J	`jnl`	Maximum Mean Discrepancy Gradient Flow. CoRR Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton
2018	J	`jnl`	Antithetic and Monte Carlo kernel estimators for partial rankings. CoRR Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani
2018	A*	`conf`	BRUNO: A Deep Recurrent Model for Exchangeable Data. NeurIPS Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre
2018	—	`conf`	Demystifying MMD GANs. ICLR (Poster) Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton
2018	J	`jnl`	Demystifying MMD GANs. CoRR Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton
2018	A	`conf`	Efficient and principled score estimation with Nyström kernel exponential families. AISTATS Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton
2018	A*	`conf`	Informative Features for Model Comparison. NeurIPS Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton
2018	J	`jnl`	Informative Features for Model Comparison. CoRR Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton
2018	A	`conf`	Kernel Conditional Exponential Family. AISTATS Michael Arbel, Arthur Gretton
2018	J	`jnl`	Kernel Exponential Family Estimation via Doubly Dual Embedding. CoRR Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He
2018	J	`jnl`	Large-scale kernel methods for independence testing. Stat. Comput. Qinyi Zhang, Sarah Filippi, Arthur Gretton, Dino Sejdinovic
2018	J	`jnl`	Learning deep kernels for exponential family densities. CoRR Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton
2018	A*	`conf`	On gradient regularizers for MMD GANs. NeurIPS Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton
2018	J	`jnl`	On gradient regularizers for MMD GANs. CoRR Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton
2017	—	`conf`	A Linear-Time Kernel Goodness-of-Fit Test. NIPS Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton
2017	J	`jnl`	A Linear-Time Kernel Goodness-of-Fit Test. CoRR Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton
2017	A*	`conf`	An Adaptive Test of Independence with Analytic Kernel Embeddings. ICML Wittawat Jitkrittum, Zoltán Szabó, Arthur Gretton
2017	J	`jnl`	Density Estimation in Infinite Dimensional Exponential Families. J. Mach. Learn. Res. Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar
2017	J	`jnl`	Efficient and principled score estimation. CoRR Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton
2017	J	`jnl`	GP-Select: Accelerating EM Using Adaptive Subspace Preselection. Neural Comput. Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Jörg Lücke, Arthur Gretton
2017	—	`conf`	Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. ICLR (Poster) Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton
2016	A	`conf`	A Kernel Test for Three-Variable Interactions with Random Processes. UAI Paul K. Rubenstein, Kacper Chwialkowski, Arthur Gretton
2016	A*	`conf`	A Kernel Test of Goodness of Fit. ICML Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton
2016	A*	`conf`	A Test of Relative Similarity For Model Selection in Generative Models. ICLR (Poster) Wacha Bounliphone, Eugene Belilovsky, Matthew B. Blaschko, Ioannis Antonoglou, Arthur Gretton
2016	J	`jnl`	An Adaptive Test of Independence with Analytic Kernel Embeddings. CoRR Wittawat Jitkrittum, Zoltán Szabó, Arthur Gretton
2016	J	`jnl`	Fast Non-Parametric Tests of Relative Dependency and Similarity. CoRR Wacha Bounliphone, Eugene Belilovsky, Arthur Tenenhaus, Ioannis Antonoglou, Arthur Gretton, Matthew B. Blaschko
2016	J	`jnl`	Filtering with State-Observation Examples via Kernel Monte Carlo Filter. Neural Comput. Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu
2016	J	`jnl`	Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. CoRR Danica J. Sutherland, Hsiao-Yu Fish Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton
2016	—	`conf`	Interpretable Distribution Features with Maximum Testing Power. NIPS Wittawat Jitkrittum, Zoltán Szabó, Kacper P. Chwialkowski, Arthur Gretton
2016	J	`jnl`	Interpretable Distribution Features with Maximum Testing Power. CoRR Wittawat Jitkrittum, Zoltán Szabó, Kacper Chwialkowski, Arthur Gretton
2016	J	`jnl`	Kernel Mean Shrinkage Estimators. J. Mach. Learn. Res. Krikamol Muandet, Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf
2016	J	`jnl`	Learning Theory for Distribution Regression. J. Mach. Learn. Res. Zoltán Szabó, Bharath K. Sriperumbudur, Barnabás Póczos, Arthur Gretton
2016	J	`jnl`	MERLiN: Mixture Effect Recovery in Linear Networks. IEEE J. Sel. Top. Signal Process. Sebastian Weichwald, Moritz Grosse-Wentrup, Arthur Gretton
2016	J	`jnl`	New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481). Dagstuhl Reports Arthur Gretton, Philipp Hennig, Carl Edward Rasmussen, Bernhard Schölkopf
2016	A	`ed.`	Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, Cadiz, Spain, May 9-11, 2016 AISTATS Arthur Gretton, Christian C. Robert
2016	—	`conf`	Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data. PRNI Sebastian Weichwald, Arthur Gretton, Bernhard Schölkopf, Moritz Grosse-Wentrup
2016	J	`jnl`	Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data. CoRR Sebastian Weichwald, Arthur Gretton, Bernhard Schölkopf, Moritz Grosse-Wentrup
2015	A*	`conf`	A low variance consistent test of relative dependency. ICML Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew B. Blaschko
2015	—	`conf`	Fast Two-Sample Testing with Analytic Representations of Probability Measures. NIPS Kacper Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, Arthur Gretton
2015	—	`conf`	Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. NIPS Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltán Szabó, Arthur Gretton
2015	A	`conf`	Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. UAI Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó
2015	J	`jnl`	Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. CoRR Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó
2015	J	`jnl`	Passing Expectation Propagation Messages with Kernel Methods. CoRR Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess
2015	A	`conf`	Two-stage sampled learning theory on distributions. AISTATS Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur
2014	A*	`conf`	A Kernel Independence Test for Random Processes. ICML Kacper Chwialkowski, Arthur Gretton
2014	—	`conf`	A Wild Bootstrap for Degenerate Kernel Tests. NIPS Kacper Chwialkowski, Dino Sejdinovic, Arthur Gretton
2014	J	`jnl`	A low variance consistent test of relative dependency. CoRR Wacha Bounliphone, Arthur Gretton, Matthew B. Blaschko
2014	J	`jnl`	Consistent, Two-Stage Sampled Distribution Regression via Mean Embedding. CoRR Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur
2014	J	`jnl`	GP-select: Accelerating EM using adaptive subspace preselection. CoRR Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Jörg Lücke, Arthur Gretton
2014	A*	`conf`	Kernel Adaptive Metropolis-Hastings. ICML Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton
2014	A*	`conf`	Kernel Mean Estimation and Stein Effect. ICML Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf
2014	J	`jnl`	Kernel Mean Shrinkage Estimators. CoRR Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf
2014	J	`jnl`	Learning Theory for Distribution Regression. CoRR Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur
2014	A*	`conf`	Monte Carlo Filtering Using Kernel Embedding of Distributions. AAAI Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu
2013	—	`conf`	A Kernel Test for Three-Variable Interactions. NIPS Dino Sejdinovic, Arthur Gretton, Wicher Bergsma
2013	—	`conf`	B-test: A Non-parametric, Low Variance Kernel Two-sample Test. NIPS Wojciech Zaremba, Arthur Gretton, Matthew B. Blaschko
2013	J	`jnl`	B-test: A Non-parametric, Low Variance Kernel Two-sample Test. CoRR Wojciech Zaremba, Arthur Gretton, Matthew B. Blaschko
2013	A	`conf`	Hilbert Space Embeddings of Predictive State Representations. UAI Byron Boots, Geoffrey J. Gordon, Arthur Gretton
2013	J	`jnl`	Hilbert Space Embeddings of Predictive State Representations. CoRR Byron Boots, Geoffrey J. Gordon, Arthur Gretton
2013	J	`jnl`	Kernel Adaptive Metropolis-Hastings. CoRR Dino Sejdinovic, Maria Lomeli Garcia, Heiko Strathmann, Christophe Andrieu, Arthur Gretton
2013	J	`jnl`	Kernel Bayes' rule: Bayesian inference with positive definite kernels. J. Mach. Learn. Res. Kenji Fukumizu, Le Song, Arthur Gretton
2013	J	`jnl`	Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models. IEEE Signal Process. Mag. Le Song, Kenji Fukumizu, Arthur Gretton
2013	J	`jnl`	Kernel Mean Estimation and Stein's Effect. CoRR Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf
2013	—	`conf`	Smooth Operators. ICML (3) Steffen Grünewälder, Arthur Gretton, John Shawe-Taylor
2013	—	`conf`	Taxonomic Prediction with Tree-Structured Covariances. ECML/PKDD (2) Matthew B. Blaschko, Wojciech Zaremba, Arthur Gretton
2012	J	`jnl`	A Kernel Two-Sample Test. J. Mach. Learn. Res. Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola
2012	J	`jnl`	Conditional mean embeddings as regressors - supplementary CoRR Steffen Grünewälder, Guy Lever, Luca Baldassarre, Sam Patterson, Arthur Gretton, Massimiliano Pontil
2012	A*	`conf`	Conditional mean embeddings as regressors. ICML Steffen Grünewälder, Guy Lever, Arthur Gretton, Luca Baldassarre, Sam Patterson, Massimiliano Pontil
2012	J	`jnl`	Equivalence of distance-based and RKHS-based statistics in hypothesis testing CoRR Dino Sejdinovic, Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu
2012	J	`jnl`	Feature Selection via Dependence Maximization. J. Mach. Learn. Res. Le Song, Alexander J. Smola, Arthur Gretton, Justin Bedo, Karsten M. Borgwardt
2012	J	`jnl`	Hilbert Space Embeddings of POMDPs CoRR Yu Nishiyama, Abdeslam Boularias, Arthur Gretton, Kenji Fukumizu
2012	A	`conf`	Hilbert Space Embeddings of POMDPs. UAI Yu Nishiyama, Abdeslam Boularias, Arthur Gretton, Kenji Fukumizu
2012	J	`jnl`	Hypothesis testing using pairwise distances and associated kernels (with Appendix) CoRR Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu
2012	A*	`conf`	Hypothesis testing using pairwise distances and associated kernels. ICML Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu
2012	J	`jnl`	Modelling transition dynamics in MDPs with RKHS embeddings CoRR Steffen Grünewälder, Guy Lever, Luca Baldassarre, Massimiliano Pontil, Arthur Gretton
2012	A*	`conf`	Modelling transition dynamics in MDPs with RKHS embeddings. ICML Steffen Grünewälder, Guy Lever, Luca Baldassarre, Massimiliano Pontil, Arthur Gretton
2012	—	`conf`	Optimal kernel choice for large-scale two-sample tests. NIPS Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu
2011	—	`conf`	Kernel Bayes' Rule. NIPS Kenji Fukumizu, Le Song, Arthur Gretton
2011	J	`jnl`	Kernel Belief Propagation CoRR Le Song, Arthur Gretton, Danny Bickson, Yucheng Low, Carlos Guestrin
2011	A	`conf`	Kernel Belief Propagation. AISTATS Le Song, Arthur Gretton, Danny Bickson, Yucheng Low, Carlos Guestrin
2011	A	`conf`	Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees. AISTATS Joseph Gonzalez, Yucheng Low, Arthur Gretton, Carlos Guestrin
2011	J	`jnl`	Semi-supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognit. Lett. Matthew B. Blaschko, Jacquelyn A. Shelton, Andreas M. Bartels, Christoph H. Lampert, Arthur Gretton
2010	—	`conf`	Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition. ECML/PKDD (1) Somayeh Danafar, Arthur Gretton, Jürgen Schmidhuber
2010	J	`jnl`	Consistent Nonparametric Tests of Independence. J. Mach. Learn. Res. Arthur Gretton, László Györfi
2010	J	`jnl`	Discriminative frequent subgraph mining with optimality guarantees. Stat. Anal. Data Min. Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt
2010	J	`jnl`	Hilbert Space Embeddings and Metrics on Probability Measures. J. Mach. Learn. Res. Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R. G. Lanckriet
2010	B	`conf`	Non-parametric estimation of integral probability metrics. ISIT Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, Gert R. G. Lanckriet
2010	A	`conf`	Nonparametric Tree Graphical Models. AISTATS Le Song, Arthur Gretton, Carlos Guestrin
2010	J	`jnl`	Temporal kernel CCA and its application in multimodal neuronal data analysis. Mach. Learn. Felix Bießmann, Frank C. Meinecke, Arthur Gretton, Alexander Rauch, Gregor Rainer, Nikos K. Logothetis, Klaus-Robert Müller
2009	—	`conf`	A Fast, Consistent Kernel Two-Sample Test. NIPS Arthur Gretton, Kenji Fukumizu, Zaïd Harchaoui, Bharath K. Sriperumbudur
2009	J	`jnl`	A note on integral probability metrics and $\phi$-divergences CoRR Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert R. G. Lanckriet, Bernhard Schölkopf
2009	A*	`conf`	Detecting the direction of causal time series. ICML Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf
2009	J	`jnl`	Fast kernel-based independent component analysis. IEEE Trans. Signal Process. Hao Shen, Stefanie Jegelka, Arthur Gretton
2009	Misc	`conf`	Generalized Clustering via Kernel Embeddings. KI Stefanie Jegelka, Arthur Gretton, Bernhard Schölkopf, Bharath K. Sriperumbudur, Ulrike von Luxburg
2009	—	`conf`	Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions. NIPS Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Gert R. G. Lanckriet, Bernhard Schölkopf
2009	A	`conf`	Near-optimal Supervised Feature Selection among Frequent Subgraphs. SDM Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt
2009	—	`conf`	Nonlinear directed acyclic structure learning with weakly additive noise models. NIPS Robert E. Tillman, Arthur Gretton, Peter Spirtes
2008	—	`conf`	A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery. MLG@ICML Matthew B. Blaschko, Arthur Gretton
2008	J	`jnl`	A Kernel Method for the Two-Sample Problem CoRR Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola
2008	—	`conf`	Characteristic Kernels on Groups and Semigroups. NIPS Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf
2008	—	`conf`	Combining near-optimal feature selection with gSpan. MLG@ICML Karsten M. Borgwardt, Xifeng Yan, Marisa Thoma, Hong Cheng, Arthur Gretton, Le Song, Alex Smola, Jiawei Han, Philip S. Yu, Hans-Peter Kriegel
2008	A*	`conf`	Injective Hilbert Space Embeddings of Probability Measures. COLT Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert R. G. Lanckriet, Bernhard Schölkopf
2008	—	`conf`	Kernel Measures of Independence for non-iid Data. NIPS Xinhua Zhang, Le Song, Arthur Gretton, Alexander J. Smola
2008	—	`conf`	Kernel Methods for Detecting the Direction of Time Series. GfKl Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf
2008	—	`conf`	Learning Taxonomies by Dependence Maximization. NIPS Matthew B. Blaschko, Arthur Gretton
2008	B	`conf`	Nonparametric Independence Tests: Space Partitioning and Kernel Approaches. ALT Arthur Gretton, László Györfi
2008	—	`conf`	Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis. ECML/PKDD (1) Matthew B. Blaschko, Christoph H. Lampert, Arthur Gretton
2008	A*	`conf`	Tailoring density estimation via reproducing kernel moment matching. ICML Le Song, Xinhua Zhang, Alexander J. Smola, Arthur Gretton, Bernhard Schölkopf
2007	B	`conf`	A Hilbert Space Embedding for Distributions. ALT Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf
2007	B	`conf`	A Hilbert Space Embedding for Distributions. Discovery Science Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf
2007	A*	`conf`	A Kernel Approach to Comparing Distributions. AAAI Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola
2007	—	`conf`	A Kernel Statistical Test of Independence. NIPS Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alexander J. Smola
2007	A*	`conf`	A dependence maximization view of clustering. ICML Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt
2007	—	`conf`	Colored Maximum Variance Unfolding. NIPS Le Song, Alexander J. Smola, Karsten M. Borgwardt, Arthur Gretton
2007	A	`conf`	Fast Kernel ICA using an Approximate Newton Method. AISTATS Hao Shen, Stefanie Jegelka, Arthur Gretton
2007	—	`conf`	Gene selection via the BAHSIC family of algorithms. ISMB/ECCB (Supplement of Bioinformatics) Le Song, Justin Bedo, Karsten M. Borgwardt, Arthur Gretton, Alexander J. Smola
2007	—	`conf`	Kernel Measures of Conditional Dependence. NIPS Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf
2007	J	`jnl`	Statistical Consistency of Kernel Canonical Correlation Analysis. J. Mach. Learn. Res. Kenji Fukumizu, Francis R. Bach, Arthur Gretton
2007	J	`jnl`	Supervised Feature Selection via Dependence Estimation CoRR Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo
2007	A*	`conf`	Supervised feature selection via dependence estimation. ICML Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo
2006	—	`conf`	A Kernel Method for the Two-Sample-Problem. NIPS Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola
2006	J	`jnl`	An online support vector machine for abnormal events detection. Signal Process. Manuel Davy, Frédéric Desobry, Arthur Gretton, Christian Doncarli
2006	—	`conf`	Correcting Sample Selection Bias by Unlabeled Data. NIPS Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf
2006	—	`conf`	Integrating structured biological data by Kernel Maximum Mean Discrepancy. ISMB (Supplement of Bioinformatics) Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola
2005	A	`conf`	Kernel Constrained Covariance for Dependence Measurement. AISTATS Arthur Gretton, Alexander J. Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schölkopf, Nikos K. Logothetis
2005	J	`jnl`	Kernel Methods for Measuring Independence. J. Mach. Learn. Res. Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf
2005	B	`conf`	Measuring Statistical Dependence with Hilbert-Schmidt Norms. ALT Arthur Gretton, Olivier Bousquet, Alexander J. Smola, Bernhard Schölkopf
2005	—	`conf`	Statistical Convergence of Kernel CCA. NIPS Kenji Fukumizu, Francis R. Bach, Arthur Gretton
2004	—	`conf`	Multivariate Regression via Stiefel Manifold Constraints. DAGM-Symposium Gökhan H. Bakir, Arthur Gretton, Matthias O. Franz, Bernhard Schölkopf
2003	—	`conf`	On-line one-class support vector machines. An application to signal segmentation. ICASSP (2) Arthur Gretton, Frédéric Desobry
2003	—	`conf`	Ranking on Data Manifolds. NIPS Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf
2003	—	`conf`	The kernel mutual information. ICASSP (4) Arthur Gretton, Ralf Herbrich, Alexander J. Smola
2002	J	`jnl`	Optimized support vector machines for nonstationary signal classification. IEEE Signal Process. Lett. Manuel Davy, Arthur Gretton, Arnaud Doucet, Peter J. W. Rayner