Optimized support vector machines for nonstationary signal classification. - Manuel Davy; Arthur Gretton; Arnaud Doucet; Peter J. W. Rayner (IEEE Signal Process. Lett., 2002)

On-line one-class support vector machines. An application to signal segmentation. - Arthur Gretton; Frédéric Desobry (ICASSP (2), 2003)

The kernel mutual information. - Arthur Gretton; Ralf Herbrich; Alexander J. Smola (ICASSP (4), 2003)

Ranking on Data Manifolds. - Dengyong Zhou; Jason Weston; Arthur Gretton; Olivier Bousquet; Bernhard Schölkopf (NIPS, 2003)

Multivariate Regression via Stiefel Manifold Constraints. - Gökhan H. Bakir; Arthur Gretton; Matthias O. Franz; Bernhard Schölkopf (DAGM-Symposium, 2004)

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

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

Statistical Convergence of Kernel CCA. - Kenji Fukumizu; Francis R. Bach; Arthur Gretton (NIPS, 2005)

Kernel Methods for Measuring Independence. - Arthur Gretton; Ralf Herbrich; Alexander J. Smola; Olivier Bousquet; Bernhard Schölkopf (J. Mach. Learn. Res., 2005)

Integrating structured biological data by Kernel Maximum Mean Discrepancy. - Karsten M. Borgwardt; Arthur Gretton; Malte J. Rasch; Hans-Peter Kriegel; Bernhard Schölkopf; Alexander J. Smola (ISMB (Supplement of Bioinformatics), 2006)

A Kernel Method for the Two-Sample-Problem. - Arthur Gretton; Karsten M. Borgwardt; Malte J. Rasch; Bernhard Schölkopf; Alexander J. Smola (NIPS, 2006)

Correcting Sample Selection Bias by Unlabeled Data. - Jiayuan Huang; Alexander J. Smola; Arthur Gretton; Karsten M. Borgwardt; Bernhard Schölkopf (NIPS, 2006)

An online support vector machine for abnormal events detection. - Manuel Davy; Frédéric Desobry; Arthur Gretton; Christian Doncarli (Signal Process., 2006)

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

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

A dependence maximization view of clustering. - Le Song; Alexander J. Smola; Arthur Gretton; Karsten M. Borgwardt (ICML, 2007)

Supervised feature selection via dependence estimation. - Le Song; Alexander J. Smola; Arthur Gretton; Karsten M. Borgwardt; Justin Bedo (ICML, 2007)

Gene selection via the BAHSIC family of algorithms. - Le Song; Justin Bedo; Karsten M. Borgwardt; Arthur Gretton; Alexander J. Smola (ISMB/ECCB (Supplement of Bioinformatics), 2007)

Kernel Measures of Conditional Dependence. - Kenji Fukumizu; Arthur Gretton; Xiaohai Sun; Bernhard Schölkopf (NIPS, 2007)

A Kernel Statistical Test of Independence. - Arthur Gretton; Kenji Fukumizu; Choon Hui Teo; Le Song; Bernhard Schölkopf; Alexander J. Smola (NIPS, 2007)

Colored Maximum Variance Unfolding. - Le Song; Alexander J. Smola; Karsten M. Borgwardt; Arthur Gretton (NIPS, 2007)

Statistical Consistency of Kernel Canonical Correlation Analysis. - Kenji Fukumizu; Francis R. Bach; Arthur Gretton (J. Mach. Learn. Res., 2007)

Fast Kernel ICA using an Approximate Newton Method. - Hao Shen 002; Stefanie Jegelka; Arthur Gretton (AISTATS, 2007)

Nonparametric Independence Tests: Space Partitioning and Kernel Approaches. - Arthur Gretton; László Györfi (ALT, 2008)

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

Kernel Methods for Detecting the Direction of Time Series. - Jonas Peters; Dominik Janzing; Arthur Gretton; Bernhard Schölkopf (GfKl, 2008)

Tailoring density estimation via reproducing kernel moment matching. - Le Song; Xinhua Zhang; Alexander J. Smola; Arthur Gretton; Bernhard Schölkopf (ICML, 2008)

A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery. - Matthew B. Blaschko; Arthur Gretton (MLG@ICML, 2008)

Combining near-optimal feature selection with gSpan. - Karsten M. Borgwardt; Xifeng Yan; Marisa Thoma; Hong Cheng 001; Arthur Gretton; Le Song; Alex Smola; Jiawei Han 001; Philip S. Yu; Hans-Peter Kriegel (MLG@ICML, 2008)

Learning Taxonomies by Dependence Maximization. - Matthew B. Blaschko; Arthur Gretton (NIPS, 2008)

Characteristic Kernels on Groups and Semigroups. - Kenji Fukumizu; Bharath K. Sriperumbudur; Arthur Gretton; Bernhard Schölkopf (NIPS, 2008)

Kernel Measures of Independence for non-iid Data. - Xinhua Zhang; Le Song; Arthur Gretton; Alexander J. Smola (NIPS, 2008)

Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis. - Matthew B. Blaschko; Christoph H. Lampert; Arthur Gretton (ECML/PKDD (1), 2008)

Detecting the direction of causal time series. - Jonas Peters; Dominik Janzing; Arthur Gretton; Bernhard Schölkopf (ICML, 2009)

Generalized Clustering via Kernel Embeddings. - Stefanie Jegelka; Arthur Gretton; Bernhard Schölkopf; Bharath K. Sriperumbudur; Ulrike von Luxburg (KI, 2009)

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

Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions. - Bharath K. Sriperumbudur; Kenji Fukumizu; Arthur Gretton; Gert R. G. Lanckriet; Bernhard Schölkopf (NIPS, 2009)

Nonlinear directed acyclic structure learning with weakly additive noise models. - Robert E. Tillman; Arthur Gretton; Peter Spirtes (NIPS, 2009)

Near-optimal Supervised Feature Selection among Frequent Subgraphs. - Marisa Thoma; Hong Cheng 001; Arthur Gretton; Jiawei Han 001; Hans-Peter Kriegel; Alexander J. Smola; Le Song; Philip S. Yu; Xifeng Yan; Karsten M. Borgwardt (SDM, 2009)

A note on integral probability metrics and $ phi$-divergences - Bharath K. Sriperumbudur; Arthur Gretton; Kenji Fukumizu; Gert R. G. Lanckriet; Bernhard Schölkopf (CoRR, 2009)

Fast kernel-based independent component analysis. - Hao Shen 002; Stefanie Jegelka; Arthur Gretton (IEEE Trans. Signal Process., 2009)

Non-parametric estimation of integral probability metrics. - Bharath K. Sriperumbudur; Kenji Fukumizu; Arthur Gretton; Bernhard Schölkopf; Gert R. G. Lanckriet (ISIT, 2010)

Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition. - Somayeh Danafar; Arthur Gretton; Jürgen Schmidhuber (ECML/PKDD (1), 2010)

Consistent Nonparametric Tests of Independence. - Arthur Gretton; László Györfi (J. Mach. Learn. Res., 2010)

Nonparametric Tree Graphical Models. - Le Song; Arthur Gretton; Carlos Guestrin (AISTATS, 2010)

Hilbert Space Embeddings and Metrics on Probability Measures. - Bharath K. Sriperumbudur; Arthur Gretton; Kenji Fukumizu; Bernhard Schölkopf; Gert R. G. Lanckriet (J. Mach. Learn. Res., 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 (Mach. Learn., 2010)

Discriminative frequent subgraph mining with optimality guarantees. - Marisa Thoma; Hong Cheng 001; Arthur Gretton; Jiawei Han 001; Hans-Peter Kriegel; Alexander J. Smola; Le Song; Philip S. Yu; Xifeng Yan; Karsten M. Borgwardt (Stat. Anal. Data Min., 2010)

Kernel Bayes' Rule. - Kenji Fukumizu; Le Song; Arthur Gretton (NIPS, 2011)

Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees. - Joseph Gonzalez 001; Yucheng Low; Arthur Gretton; Carlos Guestrin (AISTATS, 2011)

Kernel Belief Propagation. - Le Song; Arthur Gretton; Danny Bickson; Yucheng Low; Carlos Guestrin (AISTATS, 2011)

Semi-supervised kernel canonical correlation analysis with application to human fMRI. - Matthew B. Blaschko; Jacquelyn A. Shelton; Andreas M. Bartels; Christoph H. Lampert; Arthur Gretton (Pattern Recognit. Lett., 2011)

Modelling transition dynamics in MDPs with RKHS embeddings. - Steffen Grünewälder; Guy Lever; Luca Baldassarre; Massimiliano Pontil; Arthur Gretton (ICML, 2012)

Conditional mean embeddings as regressors. - Steffen Grünewälder; Guy Lever; Arthur Gretton; Luca Baldassarre; Sam Patterson; Massimiliano Pontil (ICML, 2012)

Hypothesis testing using pairwise distances and associated kernels. - Dino Sejdinovic; Arthur Gretton; Bharath K. Sriperumbudur; Kenji Fukumizu (ICML, 2012)

Optimal kernel choice for large-scale two-sample tests. - Arthur Gretton; Bharath K. Sriperumbudur; Dino Sejdinovic; Heiko Strathmann; Sivaraman Balakrishnan; Massimiliano Pontil; Kenji Fukumizu (NIPS, 2012)

Hilbert Space Embeddings of POMDPs. - Yu Nishiyama; Abdeslam Boularias; Arthur Gretton; Kenji Fukumizu (UAI, 2012)

Hypothesis testing using pairwise distances and associated kernels (with Appendix) - Dino Sejdinovic; Arthur Gretton; Bharath K. Sriperumbudur; Kenji Fukumizu (CoRR, 2012)

Conditional mean embeddings as regressors - supplementary - Steffen Grünewälder; Guy Lever; Luca Baldassarre; Sam Patterson; Arthur Gretton; Massimiliano Pontil (CoRR, 2012)

Equivalence of distance-based and RKHS-based statistics in hypothesis testing - Dino Sejdinovic; Bharath K. Sriperumbudur; Arthur Gretton; Kenji Fukumizu (CoRR, 2012)

A Kernel Two-Sample Test. - Arthur Gretton; Karsten M. Borgwardt; Malte J. Rasch; Bernhard Schölkopf; Alexander J. Smola (J. Mach. Learn. Res., 2012)

Feature Selection via Dependence Maximization. - Le Song; Alexander J. Smola; Arthur Gretton; Justin Bedo; Karsten M. Borgwardt (J. Mach. Learn. Res., 2012)

Smooth Operators. - Steffen Grünewälder; Arthur Gretton; John Shawe-Taylor (ICML (3), 2013)

A Kernel Test for Three-Variable Interactions. - Dino Sejdinovic; Arthur Gretton; Wicher Bergsma (NIPS, 2013)

B-test: A Non-parametric, Low Variance Kernel Two-sample Test. - Wojciech Zaremba; Arthur Gretton; Matthew B. Blaschko (NIPS, 2013)

Taxonomic Prediction with Tree-Structured Covariances. - Matthew B. Blaschko; Wojciech Zaremba; Arthur Gretton (ECML/PKDD (2), 2013)

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

Kernel Mean Estimation and Stein's Effect. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Arthur Gretton; Bernhard Schölkopf (CoRR, 2013)

Kernel Bayes' rule: Bayesian inference with positive definite kernels. - Kenji Fukumizu; Le Song; Arthur Gretton (J. Mach. Learn. Res., 2013)

Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models. - Le Song; Kenji Fukumizu; Arthur Gretton (IEEE Signal Process. Mag., 2013)

Monte Carlo Filtering Using Kernel Embedding of Distributions. - Motonobu Kanagawa; Yu Nishiyama; Arthur Gretton; Kenji Fukumizu (AAAI, 2014)

A Kernel Independence Test for Random Processes. - Kacper Chwialkowski; Arthur Gretton (ICML, 2014)

Kernel Mean Estimation and Stein Effect. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Arthur Gretton; Bernhard Schölkopf (ICML, 2014)

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

A Wild Bootstrap for Degenerate Kernel Tests. - Kacper Chwialkowski; Dino Sejdinovic; Arthur Gretton (NIPS, 2014)

A low variance consistent test of relative dependency. - Wacha Bounliphone; Arthur Gretton; Matthew B. Blaschko (CoRR, 2014)

Consistent, Two-Stage Sampled Distribution Regression via Mean Embedding. - Zoltán Szabó 001; Arthur Gretton; Barnabás Póczos; Bharath K. Sriperumbudur (CoRR, 2014)

Two-stage sampled learning theory on distributions. - Zoltán Szabó 001; Arthur Gretton; Barnabás Póczos; Bharath K. Sriperumbudur (AISTATS, 2015)

A low variance consistent test of relative dependency. - Wacha Bounliphone; Arthur Gretton; Arthur Tenenhaus; Matthew B. Blaschko (ICML, 2015)

Fast Two-Sample Testing with Analytic Representations of Probability Measures. - Kacper Chwialkowski; Aaditya Ramdas; Dino Sejdinovic; Arthur Gretton (NIPS, 2015)

Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. - Heiko Strathmann; Dino Sejdinovic; Samuel Livingstone; Zoltán Szabó 001; Arthur Gretton (NIPS, 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 001 (UAI, 2015)

Passing Expectation Propagation Messages with Kernel Methods. - Wittawat Jitkrittum; Arthur Gretton; Nicolas Heess (CoRR, 2015)

A Kernel Test of Goodness of Fit. - Kacper Chwialkowski; Heiko Strathmann; Arthur Gretton (ICML, 2016)

Interpretable Distribution Features with Maximum Testing Power. - Wittawat Jitkrittum; Zoltán Szabó 001; Kacper P. Chwialkowski; Arthur Gretton (NIPS, 2016)

Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data. - Sebastian Weichwald; Arthur Gretton; Bernhard Schölkopf; Moritz Grosse-Wentrup (PRNI, 2016)

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

A Test of Relative Similarity For Model Selection in Generative Models. - Wacha Bounliphone; Eugene Belilovsky; Matthew B. Blaschko; Ioannis Antonoglou; Arthur Gretton (ICLR (Poster), 2016)

Fast Non-Parametric Tests of Relative Dependency and Similarity. - Wacha Bounliphone; Eugene Belilovsky; Arthur Tenenhaus; Ioannis Antonoglou; Arthur Gretton; Matthew B. Blaschko (CoRR, 2016)

New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481). - Arthur Gretton; Philipp Hennig; Carl Edward Rasmussen; Bernhard Schölkopf (Dagstuhl Reports, 2016)

Kernel Mean Shrinkage Estimators. - Krikamol Muandet; Bharath K. Sriperumbudur; Kenji Fukumizu; Arthur Gretton; Bernhard Schölkopf (J. Mach. Learn. Res., 2016)

Learning Theory for Distribution Regression. - Zoltán Szabó 001; Bharath K. Sriperumbudur; Barnabás Póczos; Arthur Gretton (J. Mach. Learn. Res., 2016)

MERLiN: Mixture Effect Recovery in Linear Networks. - Sebastian Weichwald; Moritz Grosse-Wentrup; Arthur Gretton (IEEE J. Sel. Top. Signal Process., 2016)

Filtering with State-Observation Examples via Kernel Monte Carlo Filter. - Motonobu Kanagawa; Yu Nishiyama; Arthur Gretton; Kenji Fukumizu (Neural Comput., 2016)

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 (ICLR (Poster), 2017)

An Adaptive Test of Independence with Analytic Kernel Embeddings. - Wittawat Jitkrittum; Zoltán Szabó 001; Arthur Gretton (ICML, 2017)

A Linear-Time Kernel Goodness-of-Fit Test. - Wittawat Jitkrittum; Wenkai Xu; Zoltán Szabó 001; Kenji Fukumizu; Arthur Gretton (NIPS, 2017)

Efficient and principled score estimation. - Danica J. Sutherland; Heiko Strathmann; Michael Arbel; Arthur Gretton (CoRR, 2017)

Density Estimation in Infinite Dimensional Exponential Families. - Bharath K. Sriperumbudur; Kenji Fukumizu; Arthur Gretton; Aapo Hyvärinen; Revant Kumar (J. Mach. Learn. Res., 2017)

GP-Select: Accelerating EM Using Adaptive Subspace Preselection. - Jacquelyn A. Shelton; Jan Gasthaus; Zhenwen Dai; Jörg Lücke; Arthur Gretton (Neural Comput., 2017)

Kernel Conditional Exponential Family. - 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 (AISTATS, 2018)

Demystifying MMD GANs. - Mikolaj Binkowski; Danica J. Sutherland; Michael Arbel; Arthur Gretton (ICLR (Poster), 2018)

On gradient regularizers for MMD GANs. - Michael Arbel; Danica J. Sutherland; Mikolaj Binkowski; Arthur Gretton (NeurIPS, 2018)

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

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

Large-scale kernel methods for independence testing. - Qinyi Zhang; Sarah Filippi; Arthur Gretton; Dino Sejdinovic (Stat. Comput., 2018)

Kernel Exponential Family Estimation via Doubly Dual Embedding. - Bo Dai 001; Hanjun Dai; Arthur Gretton; Le Song; Dale Schuurmans; Niao He (AISTATS, 2019)

A maximum-mean-discrepancy goodness-of-fit test for censored data. - Tamara Fernandez; Arthur Gretton (AISTATS, 2019)

Learning deep kernels for exponential family densities. - Wenliang Li; Danica J. Sutherland; Heiko Strathmann; Arthur Gretton (ICML, 2019)

Maximum Mean Discrepancy Gradient Flow. - Michael Arbel; Anna Korba; Adil Salim; Arthur Gretton (NeurIPS, 2019)

Exponential Family Estimation via Adversarial Dynamics Embedding. - Bo Dai 001; Zhen Liu 019; Hanjun Dai; Niao He; Arthur Gretton; Le Song; Dale Schuurmans (NeurIPS, 2019)

Kernel Instrumental Variable Regression. - Rahul Singh; Maneesh Sahani; Arthur Gretton (NeurIPS, 2019)

A Kernel Stein Test for Comparing Latent Variable Models. - Heishiro Kanagawa; Wittawat Jitkrittum; Lester Mackey; Kenji Fukumizu; Arthur Gretton (CoRR, 2019)

Counterfactual Distribution Regression for Structured Inference. - Nicolò Colombo; Ricardo Silva 001; Soong Moon Kang; Arthur Gretton (CoRR, 2019)

Antithetic and Monte Carlo kernel estimators for partial rankings. - Maria Lomeli; Mark Rowland 001; Arthur Gretton; Zoubin Ghahramani (Stat. Comput., 2019)

A case for new neural network smoothness constraints. - Mihaela Rosca; Theophane Weber; Arthur Gretton; Shakir Mohamed (ICBINB@NeurIPS, 2020)

Kernelized Wasserstein Natural Gradient. - Michael Arbel; Arthur Gretton; Wuchen Li; Guido Montúfar (ICLR, 2020)

Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data. - Tamara Fernandez; Nicolas Rivera; Wenkai Xu; Arthur Gretton (ICML, 2020)

Learning Deep Kernels for Non-Parametric Two-Sample Tests. - Feng Liu 003; Wenkai Xu; Jie Lu 001; Guangquan Zhang 001; Arthur Gretton; Danica J. Sutherland (ICML, 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 (NeurIPS, 2020)

KALE: When Energy-Based Learning Meets Adversarial Training. - Michael Arbel; Liang Zhou; Arthur Gretton (CoRR, 2020)

Layer-wise Learning of Kernel Dependence Networks. - Chieh Wu; Aria Masoomi; Arthur Gretton; Jennifer G. Dy (CoRR, 2020)

Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and Off-Policy Planning. - Rahul Singh; Liyuan Xu; Arthur Gretton (CoRR, 2020)

Kernel Dependence Network. - Chieh Wu; Aria Masoomi; Arthur Gretton; Jennifer G. Dy (CoRR, 2020)

Conditional BRUNO: A neural process for exchangeable labelled data. - Iryna Korshunova; Yarin Gal; Arthur Gretton; Joni Dambre (Neurocomputing, 2020)

Model-based kernel sum rule: kernel Bayesian inference with probabilistic models. - Yu Nishiyama; Motonobu Kanagawa; Arthur Gretton; Kenji Fukumizu (Mach. Learn., 2020)

Generalized Energy Based Models. - Michael Arbel; Liang Zhou; Arthur Gretton (ICLR, 2021)

Efficient Wasserstein Natural Gradients for Reinforcement Learning. - Ted Moskovitz; Michael Arbel; Ferenc Huszar; Arthur Gretton (ICLR, 2021)

Learning Deep Features in Instrumental Variable Regression. - Liyuan Xu; Yutian Chen 001; Siddarth Srinivasan; Nando de Freitas; Arnaud Doucet; Arthur Gretton (ICLR, 2021)

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. - Afsaneh Mastouri; Yuchen Zhu; Limor Gultchin; Anna Korba; Ricardo Silva 001; Matt J. Kusner; Arthur Gretton; Krikamol Muandet (ICML, 2021)

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

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

Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation. - Liyuan Xu; Heishiro Kanagawa; Arthur Gretton (NeurIPS, 2021)

A weaker faithfulness assumption based on triple interactions. - Alexander Marx 001; Arthur Gretton; Joris M. Mooij (UAI, 2021)

Towards an Understanding of Benign Overfitting in Neural Networks. - Zhu Li; Zhi-Hua Zhou; Arthur Gretton (CoRR, 2021)

Kernel Methods for Multistage Causal Inference: Mediation Analysis and Dynamic Treatment Effects. - Rahul Singh; Liyuan Xu; Arthur Gretton (CoRR, 2021)

Composite Goodness-of-fit Tests with Kernels. - Oscar Key; Tamara Fernandez; Arthur Gretton; François-Xavier Briol (CoRR, 2021)

Deep Layer-wise Networks Have Closed-Form Weights. - Chieh Tzu Wu; Aria Masoomi; Arthur Gretton; Jennifer G. Dy (AISTATS, 2022)

Importance Weighted Kernel Bayes' Rule. - Liyuan Xu; Yutian Chen; Arnaud Doucet; Arthur Gretton (ICML, 2022)

Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection. - Lisa M. Koch; Christian M. Schürch; Arthur Gretton; Philipp Berens (MIDL, 2022)

Optimal Rates for Regularized Conditional Mean Embedding Learning. - Zhu Li; Dimitri Meunier; Mattes Mollenhauer; Arthur Gretton (NeurIPS, 2022)

KSD Aggregated Goodness-of-fit Test. - Antonin Schrab; Benjamin Guedj; Arthur Gretton (NeurIPS, 2022)

Efficient Aggregated Kernel Tests using Incomplete $U$-statistics. - Antonin Schrab; Ilmun Kim; Benjamin Guedj; Arthur Gretton (NeurIPS, 2022)

Causal inference with treatment measurement error: a nonparametric instrumental variable approach. - Yuchen Zhu; Limor Gultchin; Arthur Gretton; Matt J. Kusner; Ricardo Silva 001 (UAI, 2022)

Importance Weighting Approach in Kernel Bayes' Rule. - Liyuan Xu; Yutian Chen; Arnaud Doucet; Arthur Gretton (CoRR, 2022)

Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'. - Antonin Schrab; Wittawat Jitkrittum; Zoltán Szabó 001; Dino Sejdinovic; Arthur Gretton (CoRR, 2022)

Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference. - Pierre Glaser; Michael Arbel; Arnaud Doucet; Arthur Gretton (CoRR, 2022)

Controlling Moments with Kernel Stein Discrepancies. - Heishiro Kanagawa; Arthur Gretton; Lester Mackey (CoRR, 2022)

On Instrumental Variable Regression for Deep Offline Policy Evaluation. - Yutian Chen 001; Liyuan Xu; Çaglar Gülçehre; Tom Le Paine; Arthur Gretton; Nando de Freitas; Arnaud Doucet (J. Mach. Learn. Res., 2022)

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 (AISTATS, 2023)

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

A Neural Mean Embedding Approach for Back-door and Front-door Adjustment. - Liyuan Xu; Arthur Gretton (ICLR, 2023)

A Kernel Stein Test of Goodness of Fit for Sequential Models. - Jerome Baum; Heishiro Kanagawa; Arthur Gretton (ICML, 2023)

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

Fast and scalable score-based kernel calibration tests. - Pierre Glaser; David Widmann; Fredrik Lindsten; Arthur Gretton (UAI, 2023)

Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images. - Lisa M. Koch; Christian M. Schürch; Christian F. Baumgartner; Arthur Gretton; Philipp Berens (CoRR, 2023)

Prediction under Latent Subgroup Shifts with High-Dimensional Observations. - William I. Walker; Arthur Gretton; Maneesh Sahani (CoRR, 2023)

Nonlinear Meta-Learning Can Guarantee Faster Rates. - Dimitri Meunier; Zhu Li; Arthur Gretton; Samory Kpotufe (CoRR, 2023)

MMD Aggregated Two-Sample Test. - Antonin Schrab; Ilmun Kim; Mélisande Albert; Béatrice Laurent; Benjamin Guedj; Arthur Gretton (J. Mach. Learn. Res., 2023)

Proxy Methods for Domain Adaptation. - Katherine Tsai; Stephen R. Pfohl; Olawale Salaudeen; Nicole Chiou; Matt J. Kusner; Alexander D'Amour; Sanmi Koyejo; Arthur Gretton (AISTATS, 2024)

Distributional Bellman Operators over Mean Embeddings. - Li Kevin Wenliang; Grégoire Delétang; Matthew Aitchison; Marcus Hutter; Anian Ruoss; Arthur Gretton; Mark Rowland 001 (ICML, 2024)

A Distributional Analogue to the Successor Representation. - Harley Wiltzer; Jesse Farebrother; Arthur Gretton; Yunhao Tang; André Barreto 001; Will Dabney; Marc G. Bellemare; Mark Rowland 001 (ICML, 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 (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)

Foundations of Multivariate Distributional Reinforcement Learning. - Harley Wiltzer; Jesse Farebrother; Arthur Gretton; Mark Rowland 001 (NeurIPS, 2024)

Conditional Bayesian Quadrature. - Zonghao Chen; Masha Naslidnyk; Arthur Gretton; François-Xavier Briol (UAI, 2024)

Practical Kernel Tests of Conditional Independence. - Roman Pogodin; Antonin Schrab; Yazhe Li; Danica J. Sutherland; Arthur Gretton (CoRR, 2024)

(De)-regularized Maximum Mean Discrepancy Gradient Flow. - Zonghao Chen; Aratrika Mustafi; Pierre Glaser; Anna Korba; Arthur Gretton; Bharath K. Sriperumbudur (CoRR, 2024)

Credal Two-Sample Tests of Epistemic Ignorance. - Siu Lun Chau; Antonin Schrab; Arthur Gretton; Dino Sejdinovic; Krikamol Muandet (CoRR, 2024)

Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian Processes. - Hugh Dance; Peter Orbanz; Arthur Gretton (CoRR, 2024)

Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal. - Dimitri Meunier; Zhu Li; Tim Christensen; Arthur Gretton (CoRR, 2024)

Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation. - Eleni Sgouritsa; Virginia Aglietti; Yee Whye Teh; Arnaud Doucet; Arthur Gretton; Silvia Chiappa (CoRR, 2024)

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm. - Zhu Li; Dimitri Meunier; Mattes Mollenhauer; Arthur Gretton (J. Mach. Learn. Res., 2024)

Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions. - 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; et al. (ACL (Findings), 2025)

Density Ratio-based Proxy Causal Learning Without Density Ratios. - Bariscan Bozkurt; Ben Deaner; Dimitri Meunier; Liyuan Xu; Arthur Gretton (AISTATS, 2025)

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

Spectral Representation for Causal Estimation with Hidden Confounders. - Haotian Sun; Antoine Moulin; Tongzheng Ren; Arthur Gretton; Bo Dai 001 (AISTATS, 2025)

Kernel Single Proxy Control for Deterministic Confounding. - Liyuan Xu; Arthur Gretton (AISTATS, 2025)

Deep MMD Gradient Flow without adversarial training. - Alexandre Galashov; Valentin De Bortoli; Arthur Gretton (ICLR, 2025)

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

A Unified Data Representation Learning for Non-parametric Two-sample Testing. - Xunye Tian; Liuhua Peng; Zhijian Zhou; Mingming Gong; Arthur Gretton; Feng Liu 003 (UAI, 2025)

Accelerated Diffusion Models via Speculative Sampling. - Valentin De Bortoli; Alexandre Galashov; Arthur Gretton; Arnaud Doucet (CoRR, 2025)

Distributional Diffusion Models with Scoring Rules. - Valentin De Bortoli; Alexandre Galashov; J. Swaroop Guntupalli; Guangyao Zhou; Kevin Murphy 002; Arthur Gretton; Arnaud Doucet (CoRR, 2025)

Learning-Order Autoregressive Models with Application to Molecular Graph Generation. - Zhe Wang 055; Jiaxin Shi; Nicolas Heess; Arthur Gretton; Michalis K. Titsias (CoRR, 2025)

Regularized least squares learning with heavy-tailed noise is minimax optimal. - Mattes Mollenhauer; Nicole Mücke; Dimitri Meunier; Arthur Gretton (CoRR, 2025)

Density Ratio-Free Doubly Robust Proxy Causal Learning. - Bariscan Bozkurt; Houssam Zenati; Dimitri Meunier; Liyuan Xu; Arthur Gretton (CoRR, 2025)

Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings. - Houssam Zenati; Bariscan Bozkurt; Arthur Gretton (CoRR, 2025)

Demystifying Spectral Feature Learning for Instrumental Variable Regression. - Dimitri Meunier; Antoine Moulin; Jakub Wornbard; Vladimir R. Kostic; Arthur Gretton (CoRR, 2025)