← Authors

Arthur Gretton

101 papers · 2005–2025 · 8 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (8) 🌈 Renaissance Researcher (9) πŸƒ Academic Marathon (20) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (11) πŸ—ΊοΈ Taxonomy Completionist (28) 🏠 Conference Loyalist (39) 🌟 Keyword Trendsetter Combo (13) 🀝 Dynamic Duo (14) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (17) πŸ† Keyword Champion 🌱 Topic Pioneer πŸ—ƒοΈ Keyword Collector (147) πŸ”₯ Unstoppable (21) πŸ“ˆ Trend Setter ⚑ Prolific Year (6) πŸš€ Conference Pioneer πŸ’Ž Century Club (101)

Conferences

NIPS (39) ICML (17) AISTATS (15) JMLR (15) ICLR (8) UAI (5) ACL (1) MIDL (1)

Top co-authors

Kenji Fukumizu (14) Bernhard SchΓΆlkopf (10) Le Song (10) Michael Arbel (9) Liyuan Xu (7) Bharath K. Sriperumbudur (7) Dino Sejdinovic (7) Danica J. Sutherland (7) Zoltan Szabo (6) Heiko Strathmann (6)

Research topics

Statistics (10) Probability (2)

Keywords

kernel methods (44) reproducing kernel hilbert space (29) maximum mean discrepancy (18) two-sample test (9) nonparametric test (9) causal inference (8) bayesian inference (7) exponential family (6) statistical test (6) statistical testing (6) hypothesis testing (6) goodness-of-fit test (5) markov chain monte carlo (4) hilbert-schmidt independence criterion (4) independence testing (4) independence test (4) goodness of fit (4) nonparametric inference (4) density estimation (4) hilbert space embedding (4)

Papers

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