Arthur Gretton

Fast Best-in-Class Regret for Contextual Bandits UAI 2026 Accelerated Diffusion Models via Speculative Sampling ICML 2025 A Unified Data Representation Learning for Non-parametric Two-sample Testing UAI 2025 Composite Goodness-of-fit Tests with Kernels JMLR 2025 Credal Two-Sample Tests of Epistemic Uncertainty AISTATS 2025 Deep MMD Gradient Flow without adversarial training ICLR 2025 Density Ratio-based Proxy Causal Learning Without Density Ratios AISTATS 2025 (De)-regularized Maximum Mean Discrepancy Gradient Flow JMLR 2025 Distributional Diffusion Models with Scoring Rules ICML 2025 Kernel Single Proxy Control for Deterministic Confounding AISTATS 2025 Learning-Order Autoregressive Models with Application to Molecular Graph Generation ICML 2025 Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression ICLR 2025 Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions ACL 2025 Spectral Representation for Causal Estimation with Hidden Confounders AISTATS 2025 A Distributional Analogue to the Successor Representation ICML 2024 Conditional Bayesian Quadrature UAI 2024 Distributional Bellman Operators over Mean Embeddings ICML 2024 Foundations of Multivariate Distributional Reinforcement Learning NeurIPS 2024 Mind the Graph When Balancing Data for Fairness or Robustness NeurIPS 2024 Near-Optimality of Contrastive Divergence Algorithms NeurIPS 2024 Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms NeurIPS 2024 Proxy Methods for Domain Adaptation AISTATS 2024 Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm JMLR 2024 Adapting to Latent Subgroup Shifts via Concepts and Proxies AISTATS 2023 A Kernel Stein Test of Goodness of Fit for Sequential Models ICML 2023 A Neural Mean Embedding Approach for Back-door and Front-door Adjustment ICLR 2023 Efficient Conditionally Invariant Representation Learning ICLR 2023 Fast and scalable score-based kernel calibration tests UAI 2023 MMD Aggregated Two-Sample Test JMLR 2023 MMD-Fuse: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting NeurIPS 2023 Causal inference with treatment measurement error: a nonparametric instrumental variable approach UAI 2022 Deep Layer-wise Networks Have Closed-Form Weights AISTATS 2022 Efficient Aggregated Kernel Tests using Incomplete U-statistics NeurIPS 2022 Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection MIDL 2022 Importance Weighted Kernel Bayes’ Rule ICML 2022 KSD Aggregated Goodness-of-fit Test NeurIPS 2022 On Instrumental Variable Regression for Deep Offline Policy Evaluation JMLR 2022 Optimal Rates for Regularized Conditional Mean Embedding Learning NeurIPS 2022 A weaker faithfulness assumption based on triple interactions UAI 2021 Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation NeurIPS 2021 Efficient Wasserstein Natural Gradients for Reinforcement Learning ICLR 2021 Generalized Energy Based Models ICLR 2021 KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support NeurIPS 2021 Learning Deep Features in Instrumental Variable Regression ICLR 2021 Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction ICML 2021 Self-Supervised Learning with Kernel Dependence Maximization NeurIPS 2021 A kernel test for quasi-independence NeurIPS 2020 A Non-Asymptotic Analysis for Stein Variational Gradient Descent NeurIPS 2020 Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data ICML 2020 Learning Deep Kernels for Non-Parametric Two-Sample Tests ICML 2020 A maximum-mean-discrepancy goodness-of-fit test for censored data AISTATS 2019 Exponential Family Estimation via Adversarial Dynamics Embedding NeurIPS 2019 Kernel Exponential Family Estimation via Doubly Dual Embedding AISTATS 2019 Kernel Instrumental Variable Regression NeurIPS 2019 Learning deep kernels for exponential family densities ICML 2019 Maximum Mean Discrepancy Gradient Flow NeurIPS 2019 BRUNO: A Deep Recurrent Model for Exchangeable Data NeurIPS 2018 Demystifying MMD GANs ICLR 2018 Efficient and principled score estimation with Nyström kernel exponential families AISTATS 2018 Informative Features for Model Comparison NeurIPS 2018 Kernel Conditional Exponential Family AISTATS 2018 On gradient regularizers for MMD GANs NeurIPS 2018 A Linear-Time Kernel Goodness-of-Fit Test NeurIPS 2017 An Adaptive Test of Independence with Analytic Kernel Embeddings ICML 2017 Density Estimation in Infinite Dimensional Exponential Families JMLR 2017 A Kernel Test of Goodness of Fit ICML 2016 Interpretable Distribution Features with Maximum Testing Power NeurIPS 2016 Kernel Mean Shrinkage Estimators JMLR 2016 Learning Theory for Distribution Regression JMLR 2016 A low variance consistent test of relative dependency ICML 2015 Fast Two-Sample Testing with Analytic Representations of Probability Measures NeurIPS 2015 Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families NeurIPS 2015 Two-stage sampled learning theory on distributions AISTATS 2015 A Kernel Independence Test for Random Processes ICML 2014 A Wild Bootstrap for Degenerate Kernel Tests NeurIPS 2014 Kernel Adaptive Metropolis-Hastings ICML 2014 Kernel Mean Estimation and Stein Effect ICML 2014 A Kernel Test for Three-Variable Interactions NeurIPS 2013 B-test: A Non-parametric, Low Variance Kernel Two-sample Test NeurIPS 2013 Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels JMLR 2013 A Kernel Two-Sample Test JMLR 2012 Feature Selection via Dependence Maximization JMLR 2012 Optimal kernel choice for large-scale two-sample tests NeurIPS 2012 Kernel Bayes' Rule NeurIPS 2011 Kernel Belief Propagation AISTATS 2011 Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees AISTATS 2011 Consistent Nonparametric Tests of Independence JMLR 2010 Hilbert Space Embeddings and Metrics on Probability Measures JMLR 2010 Nonparametric Tree Graphical Models AISTATS 2010 A Fast, Consistent Kernel Two-Sample Test NeurIPS 2009 Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions NeurIPS 2009 Nonlinear directed acyclic structure learning with weakly additive noise models NeurIPS 2009 Characteristic Kernels on Groups and Semigroups NeurIPS 2008 Kernel Measures of Independence for non-iid Data NeurIPS 2008 Learning Taxonomies by Dependence Maximization NeurIPS 2008 A Kernel Statistical Test of Independence NeurIPS 2007 Colored Maximum Variance Unfolding NeurIPS 2007 Kernel Measures of Conditional Dependence NeurIPS 2007 Statistical Consistency of Kernel Canonical Correlation Analysis JMLR 2007 A Kernel Method for the Two-Sample-Problem NeurIPS 2006 Correcting Sample Selection Bias by Unlabeled Data NeurIPS 2006 Kernel Methods for Measuring Independence JMLR 2005 Fast Kernel ICA using an Approximate Newton Method AISTATS 1999