
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support
We study the gradient flow for a relaxed approximation to the KullbackL...
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SelfSupervised Learning with Kernel Dependence Maximization
We approach selfsupervised learning of image representations from a sta...
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Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
Proxy causal learning (PCL) is a method for estimating the causal effect...
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Towards an Understanding of Benign Overfitting in Neural Networks
Modern machine learning models often employ a huge number of parameters ...
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On Instrumental Variable Regression for Deep Offline Policy Evaluation
We show that the popular reinforcement learning (RL) strategy of estimat...
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Stein's Method Meets Statistics: A Review of Some Recent Developments
Stein's method is a collection of tools for analysing distributional com...
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A case for new neural network smoothness constraints
How sensitive should machine learning models be to input changes? We tac...
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A kernel test for quasiindependence
We consider settings in which the data of interest correspond to pairs o...
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Kernel Dependence Network
We propose a greedy strategy to spectrally train a deep network for mult...
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A Weaker Faithfulness Assumption based on Triple Interactions
One of the core assumptions in causal discovery is the faithfulness assu...
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Learning Deep Features in Instrumental Variable Regression
Instrumental variable (IV) regression is a standard strategy for learnin...
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Efficient Wasserstein Natural Gradients for Reinforcement Learning
A novel optimization approach is proposed for application to policy grad...
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Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and OffPolicy Planning
We propose a novel framework for nonparametric policy evaluation in sta...
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Kernelized Stein Discrepancy Tests of Goodnessoffit for TimetoEvent Data
Survival Analysis and Reliability Theory are concerned with the analysis...
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A NonAsymptotic Analysis for Stein Variational Gradient Descent
We study the Stein Variational Gradient Descent (SVGD) algorithm, which ...
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Layerwise Learning of Kernel Dependence Networks
We propose a greedy strategy to train a deep network for multiclass cla...
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KALE: When EnergyBased Learning Meets Adversarial Training
Legendre duality provides a variational lowerbound for the KullbackLei...
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Learning Deep Kernels for NonParametric TwoSample Tests
We propose a class of kernelbased twosample tests, which aim to determ...
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A kernel logrank test of independence for rightcensored data
With the incorporation of new data gathering methods in clinical researc...
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Kernelized Wasserstein Natural Gradient
Many machine learning problems can be expressed as the optimization of s...
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Counterfactual Distribution Regression for Structured Inference
We consider problems in which a system receives external perturbations f...
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A Kernel Stein Test for Comparing Latent Variable Models
We propose a nonparametric, kernelbased test to assess the relative goo...
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Maximum Mean Discrepancy Gradient Flow
We construct a Wasserstein gradient flow of the maximum mean discrepancy...
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Kernel Instrumental Variable Regression
Instrumental variable regression is a strategy for learning causal relat...
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Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE...
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Learning deep kernels for exponential family densities
The kernel exponential family is a rich class of distributions,which can...
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Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum loglikelihood estimation for exponenti...
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Informative Features for Model Comparison
Given two candidate models, and a set of target observations, we address...
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A maximummeandiscrepancy goodnessoffit test for censored data
We introduce a kernelbased goodnessoffit test for censored data, wher...
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Antithetic and Monte Carlo kernel estimators for partial rankings
In the modern age, rankings data is ubiquitous and it is useful for a va...
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On gradient regularizers for MMD GANs
We propose a principled method for gradientbased regularization of the ...
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A Generative Deep Recurrent Model for Exchangeable Data
We present a novel model architecture which leverages deep learning tool...
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Demystifying MMD GANs
We investigate the training and performance of generative adversarial ne...
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Kernel Conditional Exponential Family
A nonparametric family of conditional distributions is introduced, which...
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Efficient and principled score estimation with Nyström kernel exponential families
We propose a fast method with statistical guarantees for learning an exp...
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A LinearTime Kernel GoodnessofFit Test
We propose a novel adaptive test of goodnessoffit, with computational ...
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Fast NonParametric Tests of Relative Dependency and Similarity
We introduce two novel nonparametric statistical hypothesis tests. The ...
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Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
We propose a method to optimize the representation and distinguishabilit...
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An Adaptive Test of Independence with Analytic Kernel Embeddings
A new computationally efficient dependence measure, and an adaptive stat...
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LargeScale Kernel Methods for Independence Testing
Representations of probability measures in reproducing kernel Hilbert sp...
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Interpretable Distribution Features with Maximum Testing Power
Two semimetrics on probability distributions are proposed, given as the ...
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Recovery of nonlinear causeeffect relationships from linearly mixed neuroimaging data
Causal inference concerns the identification of causeeffect relationshi...
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A Kernel Test for ThreeVariable Interactions with Random Processes
We apply a wild bootstrap method to the Lancaster threevariable interac...
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A Kernel Test of Goodness of Fit
We propose a nonparametric statistical test for goodnessoffit: given a...
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MERLiN: Mixture Effect Recovery in Linear Networks
Causal inference concerns the identification of causeeffect relationshi...
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A Test of Relative Similarity For Model Selection in Generative Models
Probabilistic generative models provide a powerful framework for represe...
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Fast TwoSample Testing with Analytic Representations of Probability Measures
We propose a class of nonparametric twosample tests with a cost linear ...
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Gradientfree Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
We propose Kernel Hamiltonian Monte Carlo (KMC), a gradientfree adaptiv...
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KernelBased JustInTime Learning for Passing Expectation Propagation Messages
We propose an efficient nonparametric strategy for learning a message op...
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A simpler condition for consistency of a kernel independence test
A statistical test of independence may be constructed using the Hilbert...
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