| Importance Weighted Kernel Bayes' Rule. | 0 | 0.34 | 2022 |
| Causal inference with treatment measurement error: a nonparametric instrumental variable approach. | 0 | 0.34 | 2022 |
| Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction | 0 | 0.34 | 2021 |
| Efficient Wasserstein Natural Gradients for Reinforcement Learning | 0 | 0.34 | 2021 |
| Generalized Energy Based Models | 0 | 0.34 | 2021 |
| A weaker faithfulness assumption based on triple interactions. | 0 | 0.34 | 2021 |
| Learning Deep Kernels for Non-Parametric Two-Sample Tests | 0 | 0.34 | 2020 |
| Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data | 0 | 0.34 | 2020 |
| A kernel test for quasi-independence | 0 | 0.34 | 2020 |
| Conditional BRUNO: A neural process for exchangeable labelled data | 0 | 0.34 | 2019 |
| Maximum Mean Discrepancy Gradient Flow. | 2 | 0.36 | 2019 |
| Exponential Family Estimation via Adversarial Dynamics Embedding. | 0 | 0.34 | 2019 |
| Kernel Instrumental Variable Regression. | 0 | 0.34 | 2019 |
| Demystifying MMD GANs. | 0 | 0.34 | 2018 |
| Learning deep kernels for exponential family densities. | 1 | 0.35 | 2018 |
| BRUNO - A Deep Recurrent Model for Exchangeable Data. | 4 | 0.40 | 2018 |
| Informative Features for Model Comparison. | 1 | 0.36 | 2018 |
| Antithetic and Monte Carlo kernel estimators for partial rankings. | 0 | 0.34 | 2018 |
| On gradient regularizers for MMD GANs. | 7 | 0.40 | 2018 |
| Density Estimation in Infinite Dimensional Exponential Families | 10 | 0.61 | 2017 |
| Efficient and principled score estimation. | 0 | 0.34 | 2017 |
| A Linear-Time Kernel Goodness-of-Fit Test. | 2 | 0.38 | 2017 |
| A Kernel Test for Three-Variable Interactions with Random Processes. | 1 | 0.35 | 2016 |
| Interpretable Distribution Features with Maximum Testing Power. | 10 | 0.57 | 2016 |
| A Kernel Test of Goodness of Fit. | 6 | 0.60 | 2016 |
| New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481). | 0 | 0.34 | 2016 |
| Filtering with State-Observation Examples via Kernel Monte Carlo Filter | 5 | 0.49 | 2016 |
| Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. | 1 | 0.34 | 2016 |
| MERLiN: Mixture Effect Recovery in Linear Networks | 1 | 0.37 | 2016 |
| A Test of Relative Similarity For Model Selection in Generative Models | 0 | 0.34 | 2016 |
| Fast Non-Parametric Tests of Relative Dependency and Similarity. | 0 | 0.34 | 2016 |
| Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. | 6 | 0.53 | 2015 |
| A Test of Relative Similarity For Model Selection in Generative Models. | 8 | 0.52 | 2015 |
| Fast Two-Sample Testing with Analytic Representations of Probability Measures | 23 | 1.06 | 2015 |
| Smooth Operators. | 0 | 0.34 | 2015 |
| Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families | 3 | 0.43 | 2015 |
| Kernel Adaptive Metropolis-Hastings. | 0 | 0.34 | 2014 |
| GP-select: Accelerating EM using adaptive subspace preselection. | 2 | 0.36 | 2014 |
| A low variance consistent test of relative dependency. | 1 | 0.35 | 2014 |
| A Wild Bootstrap for Degenerate Kernel Tests. | 7 | 0.61 | 2014 |
| A Kernel Independence Test for Random Processes. | 3 | 0.49 | 2014 |
| Consistent, Two-Stage Sampled Distribution Regression via Mean Embedding. | 1 | 0.36 | 2014 |
| Taxonomic Prediction with Tree-Structured Covariances. | 4 | 0.43 | 2013 |
| Smooth Operators. | 0 | 0.34 | 2013 |
| Kernel Mean Estimation and Stein Effect. | 7 | 0.51 | 2013 |
| Kernel Embeddings of Conditional Distributions: A Unified Kernel Framework for Nonparametric Inference in Graphical Models. | 38 | 1.79 | 2013 |
| B-test: A Non-parametric, Low Variance Kernel Two-sample Test. | 14 | 0.98 | 2013 |
| Hilbert Space Embeddings of Predictive State Representations. | 24 | 0.84 | 2013 |
| A Kernel Test for Three-Variable Interactions. | 5 | 0.46 | 2013 |
| Kernel Bayes' rule: Bayesian inference with positive definite kernels | 23 | 1.02 | 2013 |
