Gretton, Arthur

140 publications

JMLR 2025 (De)-Regularized Maximum Mean Discrepancy Gradient Flow Zonghao Chen, Aratrika Mustafi, Pierre Glaser, Anna Korba, Arthur Gretton, Bharath K. Sriperumbudur

UAI 2025 A Unified Data Representation Learning for Non-Parametric Two-Sample Testing Xunye Tian, Liuhua Peng, Zhijian Zhou, Mingming Gong, Arthur Gretton, Feng Liu

ICML 2025 Accelerated Diffusion Models via Speculative Sampling Valentin De Bortoli, Alexandre Galashov, Arthur Gretton, Arnaud Doucet

JMLR 2025 Composite Goodness-of-Fit Tests with Kernels Oscar Key, Arthur Gretton, François-Xavier Briol, Tamara Fernandez

AISTATS 2025 Credal Two-Sample Tests of Epistemic Uncertainty Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet

ICLR 2025 Deep MMD Gradient Flow Without Adversarial Training Alexandre Galashov, Valentin De Bortoli, Arthur Gretton

NeurIPS 2025 Demystifying Spectral Feature Learning for Instrumental Variable Regression Dimitri Meunier, Antoine Moulin, Jakub Wornbard, Vladimir R Kostic, Arthur Gretton

AISTATS 2025 Density Ratio-Based Proxy Causal Learning Without Density Ratios Bariscan Bozkurt, Ben Deaner, Dimitri Meunier, Liyuan Xu, Arthur Gretton

NeurIPS 2025 Density Ratio-Free Doubly Robust Proxy Causal Learning Bariscan Bozkurt, Houssam Zenati, Dimitri Meunier, Liyuan Xu, Arthur Gretton

ICML 2025 Distributional Diffusion Models with Scoring Rules Valentin De Bortoli, Alexandre Galashov, J Swaroop Guntupalli, Guangyao Zhou, Kevin Patrick Murphy, Arthur Gretton, Arnaud Doucet

NeurIPS 2025 Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings Houssam Zenati, Bariscan Bozkurt, Arthur Gretton

AISTATS 2025 Kernel Single Proxy Control for Deterministic Confounding Liyuan Xu, Arthur Gretton

ICML 2025 Learning-Order Autoregressive Models with Application to Molecular Graph Generation Zhe Wang, Jiaxin Shi, Nicolas Heess, Arthur Gretton, Michalis Titsias

NeurIPS 2025 On the Hardness of Conditional Independence Testing in Practice Zheng He, Roman Pogodin, Yazhe Li, Namrata Deka, Arthur Gretton, Danica J. Sutherland

ICLR 2025 Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression Juno Kim, Dimitri Meunier, Arthur Gretton, Taiji Suzuki, Zhu Li

NeurIPS 2025 Regularized Least Squares Learning with Heavy-Tailed Noise Is Minimax Optimal Mattes Mollenhauer, Nicole Mücke, Dimitri Meunier, Arthur Gretton

AISTATS 2025 Spectral Representation for Causal Estimation with Hidden Confounders Haotian Sun, Antoine Moulin, Tongzheng Ren, Arthur Gretton, Bo Dai

ICML 2024 A Distributional Analogue to the Successor Representation Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, Andre Barreto, Will Dabney, Marc G Bellemare, Mark Rowland

UAI 2024 Conditional Bayesian Quadrature Zonghao Chen, Masha Naslidnyk, Arthur Gretton, Francois-Xavier Briol

ICML 2024 Distributional Bellman Operators over Mean Embeddings Li Kevin Wenliang, Gregoire Deletang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland

NeurIPS 2024 Foundations of Multivariate Distributional Reinforcement Learning Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Mark Rowland

NeurIPS 2024 Mind the Graph When Balancing Data for Fairness or Robustness Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa

NeurIPS 2024 Near-Optimality of Contrastive Divergence Algorithms Pierre Glaser, Kevin Han Huang, Arthur Gretton

NeurIPS 2024 Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms Dimitri Meunier, Zikai Shen, Mattes Mollenhauer, Arthur Gretton, Zhu Li

NeurIPSW 2024 Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression Juno Kim, Dimitri Meunier, Arthur Gretton, Taiji Suzuki, Zhu Li

AISTATS 2024 Proxy Methods for Domain Adaptation Katherine Tsai, Stephen R Pfohl, Olawale Salaudeen, Nicole Chiou, Matt Kusner, Alexander D’Amour, Sanmi Koyejo, Arthur Gretton

NeurIPSW 2024 Spectral Representation for Causal Estimation with Hidden Confounders Tongzheng Ren, Haotian Sun, Antoine Moulin, Arthur Gretton, Bo Dai

JMLR 2024 Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton

ICML 2023 A Kernel Stein Test of Goodness of Fit for Sequential Models Jerome Baum, Heishiro Kanagawa, Arthur Gretton

ICLR 2023 A Neural Mean Embedding Approach for Back-Door and Front-Door Adjustment Liyuan Xu, Arthur Gretton

AISTATS 2023 Adapting to Latent Subgroup Shifts via Concepts and Proxies Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D’Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai

ICLR 2023 Efficient Conditionally Invariant Representation Learning Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton

UAI 2023 Fast and Scalable Score-Based Kernel Calibration Tests Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton

MIDL 2023 Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection Lisa M Koch, Christian M Schürch, Arthur Gretton, Philipp Berens

JMLR 2023 MMD Aggregated Two-Sample Test Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton

NeurIPS 2023 MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting Felix Biggs, Antonin Schrab, Arthur Gretton

ICMLW 2023 Prediction Under Latent Subgroup Shifts with High-Dimensional Observations William I Walker, Arthur Gretton, Maneesh Sahani

AISTATS 2022 Deep Layer-Wise Networks Have Closed-Form Weights Chieh Tzu Wu, Aria Masoomi, Arthur Gretton, Jennifer Dy

UAI 2022 Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva

NeurIPS 2022 Efficient Aggregated Kernel Tests Using Incomplete $u$-Statistics Antonin Schrab, Ilmun Kim, Benjamin Guedj, Arthur Gretton

ICML 2022 Importance Weighted Kernel Bayes’ Rule Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton

NeurIPS 2022 KSD Aggregated Goodness-of-Fit Test Antonin Schrab, Benjamin Guedj, Arthur Gretton

JMLR 2022 On Instrumental Variable Regression for Deep Offline Policy Evaluation Yutian Chen, Liyuan Xu, Caglar Gulcehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet

NeurIPS 2022 Optimal Rates for Regularized Conditional Mean Embedding Learning Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton

UAI 2021 A Weaker Faithfulness Assumption Based on Triple Interactions Alexander Marx, Arthur Gretton, Joris M. Mooij

NeurIPS 2021 Deep Proxy Causal Learning and Its Application to Confounded Bandit Policy Evaluation Liyuan Xu, Heishiro Kanagawa, Arthur Gretton

ICLR 2021 Efficient Wasserstein Natural Gradients for Reinforcement Learning Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton

ICLR 2021 Generalized Energy Based Models Michael Arbel, Liang Zhou, Arthur Gretton

NeurIPS 2021 KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support Pierre Glaser, Michael Arbel, Arthur Gretton

ICLR 2021 Learning Deep Features in Instrumental Variable Regression Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton

ICML 2021 Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt Kusner, Arthur Gretton, Krikamol Muandet

NeurIPS 2021 Self-Supervised Learning with Kernel Dependence Maximization Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton

NeurIPSW 2020 A Case for New Neural Networks Smoothness Constraints Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed

NeurIPS 2020 A Kernel Test for Quasi-Independence Tamara Fernandez, Wenkai Xu, Marc Ditzhaus, Arthur Gretton

NeurIPS 2020 A Non-Asymptotic Analysis for Stein Variational Gradient Descent Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton

ICMLW 2020 Exchangeable Models in Meta Reinforcement Learning Iryna Korshunova, Jonas Degrave, Joni Dambre, Arthur Gretton, Ferenc Huszar

ICML 2020 Kernelized Stein Discrepancy Tests of Goodness-of-Fit for Time-to-Event Data Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton

ICLR 2020 Kernelized Wasserstein Natural Gradient Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montufar

ICML 2020 Learning Deep Kernels for Non-Parametric Two-Sample Tests Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland

MLJ 2020 Model-Based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu

AISTATS 2019 A Maximum-Mean-Discrepancy Goodness-of-Fit Test for Censored Data Tamara Fernandez, Arthur Gretton

NeurIPS 2019 Exponential Family Estimation via Adversarial Dynamics Embedding Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans

AISTATS 2019 Kernel Exponential Family Estimation via Doubly Dual Embedding Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He

NeurIPS 2019 Kernel Instrumental Variable Regression Rahul Singh, Maneesh Sahani, Arthur Gretton

ICML 2019 Learning Deep Kernels for Exponential Family Densities Li Wenliang, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton

NeurIPS 2019 Maximum Mean Discrepancy Gradient Flow Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton

NeurIPS 2018 BRUNO: A Deep Recurrent Model for Exchangeable Data Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre

ICLR 2018 Demystifying MMD GANs Mikołaj Bińkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton

AISTATS 2018 Efficient and Principled Score Estimation with Nyström Kernel Exponential Families Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton

NeurIPS 2018 Informative Features for Model Comparison Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton

AISTATS 2018 Kernel Conditional Exponential Family Michael Arbel, Arthur Gretton

NeurIPS 2018 On Gradient Regularizers for MMD GANs Michael Arbel, Danica J. Sutherland, Mikołaj Bińkowski, Arthur Gretton

NeurIPS 2017 A Linear-Time Kernel Goodness-of-Fit Test Wittawat Jitkrittum, Wenkai Xu, Zoltan Szabo, Kenji Fukumizu, Arthur Gretton

ICML 2017 An Adaptive Test of Independence with Analytic Kernel Embeddings Wittawat Jitkrittum, Zoltán Szabó, Arthur Gretton

JMLR 2017 Density Estimation in Infinite Dimensional Exponential Families Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar

ICLR 2017 Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton

UAI 2016 A Kernel Test for Three-Variable Interactions with Random Processes Paul K. Rubenstein, Kacper Chwialkowski, Arthur Gretton

ICML 2016 A Kernel Test of Goodness of Fit Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton

ICLR 2016 A Test of Relative Similarity for Model Selection in Generative Models Wacha Bounliphone, Eugene Belilovsky, Matthew B. Blaschko, Ioannis Antonoglou, Arthur Gretton

NeurIPS 2016 Interpretable Distribution Features with Maximum Testing Power Wittawat Jitkrittum, Zoltán Szabó, Kacper P Chwialkowski, Arthur Gretton

JMLR 2016 Kernel Mean Shrinkage Estimators Krikamol Muandet, Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf

JMLR 2016 Learning Theory for Distribution Regression Zoltán Szabó, Bharath K. Sriperumbudur, Barnabás Póczos, Arthur Gretton

AISTATS 2016 Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, Cadiz, Spain, May 9-11, 2016 Arthur Gretton, Christian C. Robert

ICML 2015 A Low Variance Consistent Test of Relative Dependency Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew Blaschko

NeurIPS 2015 Fast Two-Sample Testing with Analytic Representations of Probability Measures Kacper P Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, Arthur Gretton

NeurIPS 2015 Gradient-Free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltan Szabo, Arthur Gretton

UAI 2015 Kernel-Based Just-in-Time Learning for Passing Expectation Propagation Messages Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó

AISTATS 2015 Two-Stage Sampled Learning Theory on Distributions Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur

ICML 2014 A Kernel Independence Test for Random Processes Kacper Chwialkowski, Arthur Gretton

NeurIPS 2014 A Wild Bootstrap for Degenerate Kernel Tests Kacper P Chwialkowski, Dino Sejdinovic, Arthur Gretton

ICML 2014 Kernel Adaptive Metropolis-Hastings Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton

ICML 2014 Kernel Mean Estimation and Stein Effect Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, Arthur Gretton, Bernhard Schoelkopf

AAAI 2014 Monte Carlo Filtering Using Kernel Embedding of Distributions Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu

NeurIPS 2013 A Kernel Test for Three-Variable Interactions Dino Sejdinovic, Arthur Gretton, Wicher Bergsma

NeurIPS 2013 B-Test: A Non-Parametric, Low Variance Kernel Two-Sample Test Wojciech Zaremba, Arthur Gretton, Matthew Blaschko

UAI 2013 Hilbert Space Embeddings of Predictive State Representations Byron Boots, Geoffrey J. Gordon, Arthur Gretton

JMLR 2013 Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels Kenji Fukumizu, Le Song, Arthur Gretton

ECML-PKDD 2013 Taxonomic Prediction with Tree-Structured Covariances Matthew B. Blaschko, Wojciech Zaremba, Arthur Gretton

JMLR 2012 A Kernel Two-Sample Test Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander Smola

ICML 2012 Conditional Mean Embeddings as Regressors Steffen Grünewälder, Guy Lever, Arthur Gretton, Luca Baldassarre, Sam Patterson, Massimiliano Pontil

JMLR 2012 Feature Selection via Dependence Maximization Le Song, Alex Smola, Arthur Gretton, Justin Bedo, Karsten Borgwardt

UAI 2012 Hilbert Space Embeddings of POMDPs Yu Nishiyama, Abdeslam Boularias, Arthur Gretton, Kenji Fukumizu

ICML 2012 Hypothesis Testing Using Pairwise Distances and Associated Kernels Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu

ICML 2012 Modelling Transition Dynamics in MDPs with RKHS Embeddings Steffen Grünewälder, Guy Lever, Luca Baldassarre, Massimiliano Pontil, Arthur Gretton

NeurIPS 2012 Optimal Kernel Choice for Large-Scale Two-Sample Tests Arthur Gretton, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu, Bharath K. Sriperumbudur

NeurIPS 2011 Kernel Bayes' Rule Kenji Fukumizu, Le Song, Arthur Gretton

AISTATS 2011 Kernel Belief Propagation Le Song, Arthur Gretton, Danny Bickson, Yucheng Low, Carlos Guestrin

AISTATS 2011 Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees Joseph Gonzalez, Yucheng Low, Arthur Gretton, Carlos Guestrin

ECML-PKDD 2010 Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition Somayeh Danafar, Arthur Gretton, Jürgen Schmidhuber

JMLR 2010 Consistent Nonparametric Tests of Independence Arthur Gretton, László Györfi

JMLR 2010 Hilbert Space Embeddings and Metrics on Probability Measures Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R.G. Lanckriet

AISTATS 2010 Nonparametric Tree Graphical Models Le Song, Arthur Gretton, Carlos Guestrin

MLJ 2010 Temporal Kernel CCA and Its Application in Multimodal Neuronal Data Analysis Felix Bießmann, Frank C. Meinecke, Arthur Gretton, Alexander Rauch, Gregor Rainer, Nikos K. Logothetis, Klaus-Robert Müller

NeurIPS 2009 A Fast, Consistent Kernel Two-Sample Test Arthur Gretton, Kenji Fukumizu, Zaïd Harchaoui, Bharath K. Sriperumbudur

ICML 2009 Detecting the Direction of Causal Time Series Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf

NeurIPS 2009 Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions Kenji Fukumizu, Arthur Gretton, Gert R. Lanckriet, Bernhard Schölkopf, Bharath K. Sriperumbudur

NeurIPS 2009 Nonlinear Directed Acyclic Structure Learning with Weakly Additive Noise Models Arthur Gretton, Peter Spirtes, Robert E. Tillman

NeurIPS 2008 Characteristic Kernels on Groups and Semigroups Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, Bharath K. Sriperumbudur

COLT 2008 Injective Hilbert Space Embeddings of Probability Measures Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert R. G. Lanckriet, Bernhard Schölkopf

NeurIPS 2008 Kernel Measures of Independence for Non-Iid Data Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smola

NeurIPS 2008 Learning Taxonomies by Dependence Maximization Matthew Blaschko, Arthur Gretton

ALT 2008 Nonparametric Independence Tests: Space Partitioning and Kernel Approaches Arthur Gretton, László Györfi

ECML-PKDD 2008 Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis Matthew B. Blaschko, Christoph H. Lampert, Arthur Gretton

ICML 2008 Tailoring Density Estimation via Reproducing Kernel Moment Matching Le Song, Xinhua Zhang, Alexander J. Smola, Arthur Gretton, Bernhard Schölkopf

ICML 2007 A Dependence Maximization View of Clustering Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt

ALT 2007 A Hilbert Space Embedding for Distributions Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf

AAAI 2007 A Kernel Approach to Comparing Distributions Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola

NeurIPS 2007 A Kernel Statistical Test of Independence Arthur Gretton, Kenji Fukumizu, Choon H. Teo, Le Song, Bernhard Schölkopf, Alex J. Smola

NeurIPS 2007 Colored Maximum Variance Unfolding Le Song, Arthur Gretton, Karsten Borgwardt, Alex J. Smola

AISTATS 2007 Fast Kernel ICA Using an Approximate Newton Method Hao Shen, Stefanie Jegelka, Arthur Gretton

NeurIPS 2007 Kernel Measures of Conditional Dependence Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf

JMLR 2007 Statistical Consistency of Kernel Canonical Correlation Analysis Kenji Fukumizu, Francis R. Bach, Arthur Gretton

ICML 2007 Supervised Feature Selection via Dependence Estimation Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo

NeurIPS 2006 A Kernel Method for the Two-Sample-Problem Arthur Gretton, Karsten Borgwardt, Malte Rasch, Bernhard Schölkopf, Alex J. Smola

NeurIPS 2006 Correcting Sample Selection Bias by Unlabeled Data Jiayuan Huang, Arthur Gretton, Karsten Borgwardt, Bernhard Schölkopf, Alex J. Smola

AISTATS 2005 Kernel Constrained Covariance for Dependence Measurement Arthur Gretton, Alexander Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schölkopf, Nikos Logothetis

JMLR 2005 Kernel Methods for Measuring Independence Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, Bernhard Schölkopf

ALT 2005 Measuring Statistical Dependence with Hilbert-Schmidt Norms Arthur Gretton, Olivier Bousquet, Alexander J. Smola, Bernhard Schölkopf

NeurIPS 2005 Statistical Convergence of Kernel CCA Kenji Fukumizu, Arthur Gretton, Francis R. Bach

NeurIPS 2003 Ranking on Data Manifolds Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf