Arthur Gretton

According to our database, Arthur Gretton authored at least 165 papers between 2002 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Proxy Methods for Domain Adaptation.

Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton

CoRR, 2024

Practical Kernel Tests of Conditional Independence.

Roman Pogodin, Antonin Schrab, Yazhe Li, Danica J. Sutherland, Arthur Gretton

CoRR, 2024

A Distributional Analogue to the Successor Representation.

Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang, André Barreto, Will Dabney, Marc G. Bellemare, Mark Rowland

CoRR, 2024

2023

MMD Aggregated Two-Sample Test.

Antonin Schrab, Ilmun Kim, Mélisande Albert, Béatrice Laurent, Benjamin Guedj, Arthur Gretton

J. Mach. Learn. Res., 2023

Distributional Bellman Operators over Mean Embeddings.

Li Kevin Wenliang, Grégoire Delétang, Matthew Aitchison, Marcus Hutter, Anian Ruoss, Arthur Gretton, Mark Rowland

CoRR, 2023

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm.

Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton

CoRR, 2023

Kernel Single Proxy Control for Deterministic Confounding.

Liyuan Xu, Arthur Gretton

CoRR, 2023

Nonlinear Meta-Learning Can Guarantee Faster Rates.

Dimitri Meunier, Zhu Li, Arthur Gretton, Samory Kpotufe

CoRR, 2023

Prediction under Latent Subgroup Shifts with High-Dimensional Observations.

William I. Walker, Arthur Gretton, Maneesh Sahani

CoRR, 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

Fast and scalable score-based kernel calibration tests.

Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton

Proceedings of the Uncertainty in Artificial Intelligence, 2023

MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting.

Felix Biggs, Antonin Schrab, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Kernel Stein Test of Goodness of Fit for Sequential Models.

Jerome Baum, Heishiro Kanagawa, Arthur Gretton

Proceedings of the International Conference on Machine Learning, 2023

A Neural Mean Embedding Approach for Back-door and Front-door Adjustment.

Liyuan Xu, Arthur Gretton

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Efficient Conditionally Invariant Representation Learning.

Roman Pogodin, Namrata Deka, Yazhe Li, Danica J. Sutherland, Victor Veitch, Arthur Gretton

Proceedings of the Eleventh International Conference on Learning Representations, 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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

On Instrumental Variable Regression for Deep Offline Policy Evaluation.

Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet

J. Mach. Learn. Res., 2022

Controlling Moments with Kernel Stein Discrepancies.

Heishiro Kanagawa, Arthur Gretton, Lester Mackey

CoRR, 2022

Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference.

Pierre Glaser, Michael Arbel, Arnaud Doucet, Arthur Gretton

CoRR, 2022

Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'.

Antonin Schrab, Wittawat Jitkrittum, Zoltán Szabó, Dino Sejdinovic, Arthur Gretton

CoRR, 2022

Importance Weighting Approach in Kernel Bayes' Rule.

Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton

CoRR, 2022

Causal inference with treatment measurement error: a nonparametric instrumental variable approach.

Yuchen Zhu, Limor Gultchin, Arthur Gretton, Matt J. Kusner, Ricardo Silva

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Efficient Aggregated Kernel Tests using Incomplete $U$-statistics.

Antonin Schrab, Ilmun Kim, Benjamin Guedj, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

KSD Aggregated Goodness-of-fit Test.

Antonin Schrab, Benjamin Guedj, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Optimal Rates for Regularized Conditional Mean Embedding Learning.

Zhu Li, Dimitri Meunier, Mattes Mollenhauer, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection.

Lisa M. Koch, Christian M. Schürch, Arthur Gretton, Philipp Berens

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022

Importance Weighted Kernel Bayes' Rule.

Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton

Proceedings of the International Conference on Machine Learning, 2022

Deep Layer-wise Networks Have Closed-Form Weights.

Chieh Tzu Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Composite Goodness-of-fit Tests with Kernels.

Oscar Key, Tamara Fernandez, Arthur Gretton, François-Xavier Briol

CoRR, 2021

Kernel Methods for Multistage Causal Inference: Mediation Analysis and Dynamic Treatment Effects.

Rahul Singh, Liyuan Xu, Arthur Gretton

CoRR, 2021

Towards an Understanding of Benign Overfitting in Neural Networks.

Zhu Li, Zhi-Hua Zhou, Arthur Gretton

CoRR, 2021

A weaker faithfulness assumption based on triple interactions.

Alexander Marx, Arthur Gretton, Joris M. Mooij

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

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

Liyuan Xu, Heishiro Kanagawa, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Self-Supervised Learning with Kernel Dependence Maximization.

Yazhe Li, Roman Pogodin, Danica J. Sutherland, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Pierre Glaser, Michael Arbel, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

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

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Deep Features in Instrumental Variable Regression.

Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton

Proceedings of the 9th International Conference on Learning Representations, 2021

Efficient Wasserstein Natural Gradients for Reinforcement Learning.

Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton

Proceedings of the 9th International Conference on Learning Representations, 2021

Generalized Energy Based Models.

Michael Arbel, Liang Zhou, Arthur Gretton

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Model-based kernel sum rule: kernel Bayesian inference with probabilistic models.

Yu Nishiyama, Motonobu Kanagawa, Arthur Gretton, Kenji Fukumizu

Mach. Learn., 2020

Conditional BRUNO: A neural process for exchangeable labelled data.

Iryna Korshunova, Yarin Gal, Arthur Gretton, Joni Dambre

Neurocomputing, 2020

Kernel Dependence Network.

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

Layer-wise Learning of Kernel Dependence Networks.

Chieh Wu, Aria Masoomi, Arthur Gretton, Jennifer G. Dy

CoRR, 2020

KALE: When Energy-Based Learning Meets Adversarial Training.

Michael Arbel, Liang Zhou, Arthur Gretton

CoRR, 2020

A Non-Asymptotic Analysis for Stein Variational Gradient Descent.

Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A kernel test for quasi-independence.

Tamara Fernandez, Wenkai Xu, Marc Ditzhaus, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Deep Kernels for Non-Parametric Two-Sample Tests.

Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland

Proceedings of the 37th International Conference on Machine Learning, 2020

Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data.

Tamara Fernandez, Nicolas Rivera, Wenkai Xu, Arthur Gretton

Proceedings of the 37th International Conference on Machine Learning, 2020

Kernelized Wasserstein Natural Gradient.

Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montúfar

Proceedings of the 8th International Conference on Learning Representations, 2020

A case for new neural network smoothness constraints.

Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed

Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

2019

Antithetic and Monte Carlo kernel estimators for partial rankings.

Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani

Stat. Comput., 2019

Counterfactual Distribution Regression for Structured Inference.

Nicolò Colombo, Ricardo Silva, Soong Moon Kang, Arthur Gretton

CoRR, 2019

A Kernel Stein Test for Comparing Latent Variable Models.

Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton

CoRR, 2019

Kernel Instrumental Variable Regression.

Rahul Singh, Maneesh Sahani, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Exponential Family Estimation via Adversarial Dynamics Embedding.

Bo Dai, Zhen Liu, Hanjun Dai, Niao He, Arthur Gretton, Le Song, Dale Schuurmans

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Maximum Mean Discrepancy Gradient Flow.

Michael Arbel, Anna Korba, Adil Salim, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning deep kernels for exponential family densities.

Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton

Proceedings of the 36th International Conference on Machine Learning, 2019

A maximum-mean-discrepancy goodness-of-fit test for censored data.

Tamara Fernandez, Arthur Gretton

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Kernel Exponential Family Estimation via Doubly Dual Embedding.

Bo Dai, Hanjun Dai, Arthur Gretton, Le Song, Dale Schuurmans, Niao He

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Large-scale kernel methods for independence testing.

Qinyi Zhang, Sarah Filippi, Arthur Gretton, Dino Sejdinovic

Stat. Comput., 2018

BRUNO: A Deep Recurrent Model for Exchangeable Data.

Iryna Korshunova, Jonas Degrave, Ferenc Huszar, Yarin Gal, Arthur Gretton, Joni Dambre

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Informative Features for Model Comparison.

Wittawat Jitkrittum, Heishiro Kanagawa, Patsorn Sangkloy, James Hays, Bernhard Schölkopf, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On gradient regularizers for MMD GANs.

Michael Arbel, Danica J. Sutherland, Mikolaj Binkowski, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Demystifying MMD GANs.

Mikolaj Binkowski, Danica J. Sutherland, Michael Arbel, Arthur Gretton

Proceedings of the 6th International Conference on Learning Representations, 2018

Efficient and principled score estimation with Nyström kernel exponential families.

Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Kernel Conditional Exponential Family.

Michael Arbel, Arthur Gretton

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

GP-Select: Accelerating EM Using Adaptive Subspace Preselection.

Jacquelyn A. Shelton, Jan Gasthaus, Zhenwen Dai, Jörg Lücke, Arthur Gretton

Neural Comput., 2017

Density Estimation in Infinite Dimensional Exponential Families.

Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar

J. Mach. Learn. Res., 2017

Efficient and principled score estimation.

Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton

CoRR, 2017

A Linear-Time Kernel Goodness-of-Fit Test.

Wittawat Jitkrittum, Wenkai Xu, Zoltán Szabó, Kenji Fukumizu, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

An Adaptive Test of Independence with Analytic Kernel Embeddings.

Wittawat Jitkrittum, Zoltán Szabó, Arthur Gretton

Proceedings of the 34th International Conference on Machine Learning, 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

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Filtering with State-Observation Examples via Kernel Monte Carlo Filter.

Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu

Neural Comput., 2016

MERLiN: Mixture Effect Recovery in Linear Networks.

Sebastian Weichwald, Moritz Grosse-Wentrup, Arthur Gretton

IEEE J. Sel. Top. Signal Process., 2016

Learning Theory for Distribution Regression.

Zoltán Szabó, Bharath K. Sriperumbudur, Barnabás Póczos, Arthur Gretton

J. Mach. Learn. Res., 2016

Kernel Mean Shrinkage Estimators.

Krikamol Muandet, Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf

J. Mach. Learn. Res., 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

Fast Non-Parametric Tests of Relative Dependency and Similarity.

Wacha Bounliphone, Eugene Belilovsky, Arthur Tenenhaus, Ioannis Antonoglou, Arthur Gretton, Matthew B. Blaschko

CoRR, 2016

A Test of Relative Similarity For Model Selection in Generative Models.

Wacha Bounliphone, Eugene Belilovsky, Matthew B. Blaschko, Ioannis Antonoglou, Arthur Gretton

Proceedings of the 4th International Conference on Learning Representations, 2016

A Kernel Test for Three-Variable Interactions with Random Processes.

Paul K. Rubenstein, Kacper Chwialkowski, Arthur Gretton

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data.

Sebastian Weichwald, Arthur Gretton, Bernhard Schölkopf, Moritz Grosse-Wentrup

Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2016

Interpretable Distribution Features with Maximum Testing Power.

Wittawat Jitkrittum, Zoltán Szabó, Kacper P. Chwialkowski, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

A Kernel Test of Goodness of Fit.

Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Passing Expectation Propagation Messages with Kernel Methods.

Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess

CoRR, 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

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families.

Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltán Szabó, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Fast Two-Sample Testing with Analytic Representations of Probability Measures.

Kacper Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A low variance consistent test of relative dependency.

Wacha Bounliphone, Arthur Gretton, Arthur Tenenhaus, Matthew B. Blaschko

Proceedings of the 32nd International Conference on Machine Learning, 2015

Two-stage sampled learning theory on distributions.

Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Consistent, Two-Stage Sampled Distribution Regression via Mean Embedding.

Zoltán Szabó, Arthur Gretton, Barnabás Póczos, Bharath K. Sriperumbudur

CoRR, 2014

A low variance consistent test of relative dependency.

Wacha Bounliphone, Arthur Gretton, Matthew B. Blaschko

CoRR, 2014

A Wild Bootstrap for Degenerate Kernel Tests.

Kacper Chwialkowski, Dino Sejdinovic, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Kernel Adaptive Metropolis-Hastings.

Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton

Proceedings of the 31th International Conference on Machine Learning, 2014

Kernel Mean Estimation and Stein Effect.

Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf

Proceedings of the 31th International Conference on Machine Learning, 2014

A Kernel Independence Test for Random Processes.

Kacper Chwialkowski, Arthur Gretton

Proceedings of the 31th International Conference on Machine Learning, 2014

Monte Carlo Filtering Using Kernel Embedding of Distributions.

Motonobu Kanagawa, Yu Nishiyama, Arthur Gretton, Kenji Fukumizu

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

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

Kernel Bayes' rule: Bayesian inference with positive definite kernels.

Kenji Fukumizu, Le Song, Arthur Gretton

J. Mach. Learn. Res., 2013

Kernel Mean Estimation and Stein's Effect.

Krikamol Muandet, Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf

CoRR, 2013

Hilbert Space Embeddings of Predictive State Representations.

Byron Boots, Geoffrey J. Gordon, Arthur Gretton

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Taxonomic Prediction with Tree-Structured Covariances.

Matthew B. Blaschko, Wojciech Zaremba, Arthur Gretton

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

B-test: A Non-parametric, Low Variance Kernel Two-sample Test.

Wojciech Zaremba, Arthur Gretton, Matthew B. Blaschko

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

A Kernel Test for Three-Variable Interactions.

Dino Sejdinovic, Arthur Gretton, Wicher Bergsma

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Smooth Operators.

Steffen Grünewälder, Arthur Gretton, John Shawe-Taylor

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Feature Selection via Dependence Maximization.

Le Song, Alexander J. Smola, Arthur Gretton, Justin Bedo, Karsten M. Borgwardt

J. Mach. Learn. Res., 2012

A Kernel Two-Sample Test.

Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola

J. Mach. Learn. Res., 2012

Equivalence of distance-based and RKHS-based statistics in hypothesis testing

Dino Sejdinovic, Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu

CoRR, 2012

Conditional mean embeddings as regressors - supplementary

Steffen Grünewälder, Guy Lever, Luca Baldassarre, Sam Patterson, Arthur Gretton, Massimiliano Pontil

CoRR, 2012

Hypothesis testing using pairwise distances and associated kernels (with Appendix)

Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu

CoRR, 2012

Hilbert Space Embeddings of POMDPs.

Yu Nishiyama, Abdeslam Boularias, Arthur Gretton, Kenji Fukumizu

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 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

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Hypothesis testing using pairwise distances and associated kernels.

Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu

Proceedings of the 29th International Conference on Machine Learning, 2012

Conditional mean embeddings as regressors.

Steffen Grünewälder, Guy Lever, Arthur Gretton, Luca Baldassarre, Sam Patterson, Massimiliano Pontil

Proceedings of the 29th International Conference on Machine Learning, 2012

Modelling transition dynamics in MDPs with RKHS embeddings.

Steffen Grünewälder, Guy Lever, Luca Baldassarre, Massimiliano Pontil, Arthur Gretton

Proceedings of the 29th International Conference on Machine Learning, 2012

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

Kernel Belief Propagation.

Le Song, Arthur Gretton, Danny Bickson, Yucheng Low, Carlos Guestrin

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees.

Joseph Gonzalez, Yucheng Low, Arthur Gretton, Carlos Guestrin

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Kernel Bayes' Rule.

Kenji Fukumizu, Le Song, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Discriminative frequent subgraph mining with optimality guarantees.

Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt

Stat. Anal. Data Min., 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

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

Nonparametric Tree Graphical Models.

Le Song, Arthur Gretton, Carlos Guestrin

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Consistent Nonparametric Tests of Independence.

Arthur Gretton, László Györfi

J. Mach. Learn. Res., 2010

Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition.

Somayeh Danafar, Arthur Gretton, Jürgen Schmidhuber

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Non-parametric estimation of integral probability metrics.

Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, Gert R. G. Lanckriet

Proceedings of the IEEE International Symposium on Information Theory, 2010

2009

Fast kernel-based independent component analysis.

Hao Shen, Stefanie Jegelka, Arthur Gretton

IEEE Trans. Signal Process., 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

Near-optimal Supervised Feature Selection among Frequent Subgraphs.

Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt

Proceedings of the SIAM International Conference on Data Mining, 2009

Nonlinear directed acyclic structure learning with weakly additive noise models.

Robert E. Tillman, Arthur Gretton, Peter Spirtes

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions.

Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Gert R. G. Lanckriet, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

A Fast, Consistent Kernel Two-Sample Test.

Arthur Gretton, Kenji Fukumizu, Zaïd Harchaoui, Bharath K. Sriperumbudur

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Generalized Clustering via Kernel Embeddings.

Stefanie Jegelka, Arthur Gretton, Bernhard Schölkopf, Bharath K. Sriperumbudur, Ulrike von Luxburg

Proceedings of the KI 2009: Advances in Artificial Intelligence, 2009

Detecting the direction of causal time series.

Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis.

Matthew B. Blaschko, Christoph H. Lampert, Arthur Gretton

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

Kernel Measures of Independence for non-iid Data.

Xinhua Zhang, Le Song, Arthur Gretton, Alexander J. Smola

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Characteristic Kernels on Groups and Semigroups.

Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Learning Taxonomies by Dependence Maximization.

Matthew B. Blaschko, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Tailoring density estimation via reproducing kernel moment matching.

Le Song, Xinhua Zhang, Alexander J. Smola, Arthur Gretton, Bernhard Schölkopf

Proceedings of the Machine Learning, 2008

Kernel Methods for Detecting the Direction of Time Series.

Jonas Peters, Dominik Janzing, Arthur Gretton, Bernhard Schölkopf

Proceedings of the Advances in Data Analysis, Data Handling and Business Intelligence, 2008

Injective Hilbert Space Embeddings of Probability Measures.

Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Gert R. G. Lanckriet, Bernhard Schölkopf

Proceedings of the 21st Annual Conference on Learning Theory, 2008

Nonparametric Independence Tests: Space Partitioning and Kernel Approaches.

Arthur Gretton, László Györfi

Proceedings of the Algorithmic Learning Theory, 19th International Conference, 2008

2007

Fast Kernel ICA using an Approximate Newton Method.

Hao Shen, Stefanie Jegelka, Arthur Gretton

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Statistical Consistency of Kernel Canonical Correlation Analysis.

Kenji Fukumizu, Francis R. Bach, Arthur Gretton

J. Mach. Learn. Res., 2007

Colored Maximum Variance Unfolding.

Le Song, Alexander J. Smola, Karsten M. Borgwardt, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

A Kernel Statistical Test of Independence.

Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alexander J. Smola

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Kernel Measures of Conditional Dependence.

Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Gene selection via the BAHSIC family of algorithms.

Le Song, Justin Bedo, Karsten M. Borgwardt, Arthur Gretton, Alexander J. Smola

Proceedings of the Proceedings 15th International Conference on Intelligent Systems for Molecular Biology (ISMB) & 6th European Conference on Computational Biology (ECCB), 2007

Supervised feature selection via dependence estimation.

Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo

Proceedings of the Machine Learning, 2007

A dependence maximization view of clustering.

Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt

Proceedings of the Machine Learning, 2007

A Hilbert Space Embedding for Distributions.

Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf

Proceedings of the Discovery Science, 10th International Conference, 2007

A Kernel Approach to Comparing Distributions.

Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola

Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2006

An online support vector machine for abnormal events detection.

Manuel Davy, Frédéric Desobry, Arthur Gretton, Christian Doncarli

Signal Process., 2006

Correcting Sample Selection Bias by Unlabeled Data.

Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

A Kernel Method for the Two-Sample-Problem.

Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

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

Proceedings of the Proceedings 14th International Conference on Intelligent Systems for Molecular Biology 2006, 2006

2005

Kernel Methods for Measuring Independence.

Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf

J. Mach. Learn. Res., 2005

Statistical Convergence of Kernel CCA.

Kenji Fukumizu, Francis R. Bach, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Measuring Statistical Dependence with Hilbert-Schmidt Norms.

Arthur Gretton, Olivier Bousquet, Alexander J. Smola, Bernhard Schölkopf

Proceedings of the Algorithmic Learning Theory, 16th International Conference, 2005

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

Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

2004

Multivariate Regression via Stiefel Manifold Constraints.

Gökhan H. Bakir, Arthur Gretton, Matthias O. Franz, Bernhard Schölkopf

Proceedings of the Pattern Recognition, 26th DAGM Symposium, August 30, 2004

2003

Ranking on Data Manifolds.

Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

The kernel mutual information.

Arthur Gretton, Ralf Herbrich, Alexander J. Smola

Proceedings of the 2003 IEEE International Conference on Acoustics, 2003

On-line one-class support vector machines. An application to signal segmentation.

Arthur Gretton, Frédéric Desobry

Proceedings of the 2003 IEEE International Conference on Acoustics, 2003

2002

Optimized support vector machines for nonstationary signal classification.

Manuel Davy, Arthur Gretton, Arnaud Doucet, Peter J. W. Rayner

IEEE Signal Process. Lett., 2002