Arthur Gretton

According to our database1, Arthur Gretton authored at least 136 papers between 2002 and 2021.

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Bibliography

2021
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support.
CoRR, 2021

Self-Supervised Learning with Kernel Dependence Maximization.
CoRR, 2021

Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation.
CoRR, 2021

Towards an Understanding of Benign Overfitting in Neural Networks.
CoRR, 2021

On Instrumental Variable Regression for Deep Offline Policy Evaluation.
CoRR, 2021

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction.
Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Deep Features in Instrumental Variable Regression.
Proceedings of the 9th International Conference on Learning Representations, 2021

Efficient Wasserstein Natural Gradients for Reinforcement Learning.
Proceedings of the 9th International Conference on Learning Representations, 2021

Generalized Energy Based Models.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Model-based kernel sum rule: kernel Bayesian inference with probabilistic models.
Mach. Learn., 2020

Conditional BRUNO: A neural process for exchangeable labelled data.
Neurocomputing, 2020

A case for new neural network smoothness constraints.
CoRR, 2020

Kernel Dependence Network.
CoRR, 2020

A Weaker Faithfulness Assumption based on Triple Interactions.
CoRR, 2020

Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and Off-Policy Planning.
CoRR, 2020

Layer-wise Learning of Kernel Dependence Networks.
CoRR, 2020

KALE: When Energy-Based Learning Meets Adversarial Training.
CoRR, 2020

A Non-Asymptotic Analysis for Stein Variational Gradient Descent.
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.
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.
Proceedings of the 37th International Conference on Machine Learning, 2020

Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data.
Proceedings of the 37th International Conference on Machine Learning, 2020

Kernelized Wasserstein Natural Gradient.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Antithetic and Monte Carlo kernel estimators for partial rankings.
Stat. Comput., 2019

Counterfactual Distribution Regression for Structured Inference.
CoRR, 2019

A Kernel Stein Test for Comparing Latent Variable Models.
CoRR, 2019

Kernel Instrumental Variable Regression.
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.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Maximum Mean Discrepancy Gradient Flow.
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.
Proceedings of the 36th International Conference on Machine Learning, 2019

A maximum-mean-discrepancy goodness-of-fit test for censored data.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Kernel Exponential Family Estimation via Doubly Dual Embedding.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Large-scale kernel methods for independence testing.
Stat. Comput., 2018

BRUNO: A Deep Recurrent Model for Exchangeable Data.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Informative Features for Model Comparison.
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.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Demystifying MMD GANs.
Proceedings of the 6th International Conference on Learning Representations, 2018

Efficient and principled score estimation with Nyström kernel exponential families.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Kernel Conditional Exponential Family.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
GP-Select: Accelerating EM Using Adaptive Subspace Preselection.
Neural Comput., 2017

Density Estimation in Infinite Dimensional Exponential Families.
J. Mach. Learn. Res., 2017

Efficient and principled score estimation.
CoRR, 2017

A Linear-Time Kernel Goodness-of-Fit Test.
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.
Proceedings of the 34th International Conference on Machine Learning, 2017

Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
Filtering with State-Observation Examples via Kernel Monte Carlo Filter.
Neural Comput., 2016

MERLiN: Mixture Effect Recovery in Linear Networks.
IEEE J. Sel. Top. Signal Process., 2016

Learning Theory for Distribution Regression.
J. Mach. Learn. Res., 2016

Kernel Mean Shrinkage Estimators.
J. Mach. Learn. Res., 2016

New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481).
Dagstuhl Reports, 2016

Fast Non-Parametric Tests of Relative Dependency and Similarity.
CoRR, 2016

A Test of Relative Similarity For Model Selection in Generative Models.
Proceedings of the 4th International Conference on Learning Representations, 2016

A Kernel Test for Three-Variable Interactions with Random Processes.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data.
Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2016

Interpretable Distribution Features with Maximum Testing Power.
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.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Passing Expectation Propagation Messages with Kernel Methods.
CoRR, 2015

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families.
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.
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.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Two-stage sampled learning theory on distributions.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Consistent, Two-Stage Sampled Distribution Regression via Mean Embedding.
CoRR, 2014

A low variance consistent test of relative dependency.
CoRR, 2014

A Wild Bootstrap for Degenerate Kernel Tests.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Kernel Adaptive Metropolis-Hastings.
Proceedings of the 31th International Conference on Machine Learning, 2014

Kernel Mean Estimation and Stein Effect.
Proceedings of the 31th International Conference on Machine Learning, 2014

A Kernel Independence Test for Random Processes.
Proceedings of the 31th International Conference on Machine Learning, 2014

Monte Carlo Filtering Using Kernel Embedding of Distributions.
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.
IEEE Signal Process. Mag., 2013

Kernel Bayes' rule: Bayesian inference with positive definite kernels.
J. Mach. Learn. Res., 2013

Kernel Mean Estimation and Stein's Effect.
CoRR, 2013

Hilbert Space Embeddings of Predictive State Representations.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Taxonomic Prediction with Tree-Structured Covariances.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

B-test: A Non-parametric, Low Variance Kernel Two-sample Test.
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.
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.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Feature Selection via Dependence Maximization.
J. Mach. Learn. Res., 2012

A Kernel Two-Sample Test.
J. Mach. Learn. Res., 2012

Equivalence of distance-based and RKHS-based statistics in hypothesis testing
CoRR, 2012

Conditional mean embeddings as regressors - supplementary
CoRR, 2012

Hypothesis testing using pairwise distances and associated kernels (with Appendix)
CoRR, 2012

Hilbert Space Embeddings of POMDPs.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Optimal kernel choice for large-scale two-sample tests.
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.
Proceedings of the 29th International Conference on Machine Learning, 2012

Conditional mean embeddings as regressors.
Proceedings of the 29th International Conference on Machine Learning, 2012

Modelling transition dynamics in MDPs with RKHS embeddings.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Semi-supervised kernel canonical correlation analysis with application to human fMRI.
Pattern Recognit. Lett., 2011

Kernel Belief Propagation.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Kernel Bayes' Rule.
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.
Stat. Anal. Data Min., 2010

Temporal kernel CCA and its application in multimodal neuronal data analysis.
Mach. Learn., 2010

Hilbert Space Embeddings and Metrics on Probability Measures.
J. Mach. Learn. Res., 2010

Nonparametric Tree Graphical Models.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Consistent Nonparametric Tests of Independence.
J. Mach. Learn. Res., 2010

Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Non-parametric estimation of integral probability metrics.
Proceedings of the IEEE International Symposium on Information Theory, 2010

2009
Fast kernel-based independent component analysis.
IEEE Trans. Signal Process., 2009

A note on integral probability metrics and $\phi$-divergences
CoRR, 2009

Near-optimal Supervised Feature Selection among Frequent Subgraphs.
Proceedings of the SIAM International Conference on Data Mining, 2009

Nonlinear directed acyclic structure learning with weakly additive noise models.
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.
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.
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.
Proceedings of the KI 2009: Advances in Artificial Intelligence, 2009

Detecting the direction of causal time series.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

Kernel Measures of Independence for non-iid Data.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Characteristic Kernels on Groups and Semigroups.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Learning Taxonomies by Dependence Maximization.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Tailoring density estimation via reproducing kernel moment matching.
Proceedings of the Machine Learning, 2008

Kernel Methods for Detecting the Direction of Time Series.
Proceedings of the Advances in Data Analysis, Data Handling and Business Intelligence, 2008

Injective Hilbert Space Embeddings of Probability Measures.
Proceedings of the 21st Annual Conference on Learning Theory, 2008

Nonparametric Independence Tests: Space Partitioning and Kernel Approaches.
Proceedings of the Algorithmic Learning Theory, 19th International Conference, 2008

2007
Fast Kernel ICA using an Approximate Newton Method.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Statistical Consistency of Kernel Canonical Correlation Analysis.
J. Mach. Learn. Res., 2007

Colored Maximum Variance Unfolding.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

A Kernel Statistical Test of Independence.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Kernel Measures of Conditional Dependence.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Gene selection via the BAHSIC family of algorithms.
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.
Proceedings of the Machine Learning, 2007

A dependence maximization view of clustering.
Proceedings of the Machine Learning, 2007

A Hilbert Space Embedding for Distributions.
Proceedings of the Discovery Science, 10th International Conference, 2007

A Kernel Approach to Comparing Distributions.
Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2006
An online support vector machine for abnormal events detection.
Signal Process., 2006

Correcting Sample Selection Bias by Unlabeled Data.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

A Kernel Method for the Two-Sample-Problem.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Integrating structured biological data by Kernel Maximum Mean Discrepancy.
Proceedings of the Proceedings 14th International Conference on Intelligent Systems for Molecular Biology 2006, 2006

2005
Kernel Methods for Measuring Independence.
J. Mach. Learn. Res., 2005

Statistical Convergence of Kernel CCA.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Measuring Statistical Dependence with Hilbert-Schmidt Norms.
Proceedings of the Algorithmic Learning Theory, 16th International Conference, 2005

Kernel Constrained Covariance for Dependence Measurement.
Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

2004
Multivariate Regression via Stiefel Manifold Constraints.
Proceedings of the Pattern Recognition, 26th DAGM Symposium, August 30, 2004

2003
Ranking on Data Manifolds.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

The kernel mutual information.
Proceedings of the 2003 IEEE International Conference on Acoustics, 2003

On-line one-class support vector machines. An application to signal segmentation.
Proceedings of the 2003 IEEE International Conference on Acoustics, 2003

2002
Optimized support vector machines for nonstationary signal classification.
IEEE Signal Process. Lett., 2002


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