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08/08/2023
Kernel Single Proxy Control for Deterministic Confounding
We consider the problem of causal effect estimation with an unobserved c...
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07/20/2023
Nonlinear Meta-Learning Can Guarantee Faster Rates
Many recent theoretical works on meta-learning aim to achieve guarantees...
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06/23/2023
Prediction under Latent Subgroup Shifts with High-Dimensional Observations
We introduce a new approach to prediction in graphical models with laten...
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06/14/2023
MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
We propose novel statistics which maximise the power of a two-sample tes...
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03/08/2023
Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images
Distribution shifts remain a fundamental problem for the safe applicatio...
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12/21/2022
Adapting to Latent Subgroup Shifts via Concepts and Proxies
We address the problem of unsupervised domain adaptation when the source...
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12/16/2022
Efficient Conditionally Invariant Representation Learning
We introduce the Conditional Independence Regression CovariancE (CIRCE),...
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11/10/2022
Controlling Moments with Kernel Stein Discrepancies
Quantifying the deviation of a probability distribution is challenging w...
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10/26/2022
Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference
We introduce two synthetic likelihood methods for Simulation-Based Infer...
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10/19/2022
A kernel Stein test of goodness of fit for sequential models
We propose a goodness-of-fit measure for probability densities modeling ...
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10/12/2022
A Neural Mean Embedding Approach for Back-door and Front-door Adjustment
We consider the estimation of average and counterfactual treatment effec...
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08/02/2022
Optimal Rates for Regularized Conditional Mean Embedding Learning
We address the consistency of a kernel ridge regression estimate of the ...
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06/18/2022
Efficient Aggregated Kernel Tests using Incomplete U-statistics
We propose a series of computationally efficient, nonparametric tests fo...
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02/05/2022
Importance Weighting Approach in Kernel Bayes' Rule
We study a nonparametric approach to Bayesian computation via feature me...
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02/02/2022
KSD Aggregated Goodness-of-fit Test
We investigate properties of goodness-of-fit tests based on the Kernel S...
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02/01/2022
Deep Layer-wise Networks Have Closed-Form Weights
There is currently a debate within the neuroscience community over the l...
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11/19/2021
Composite Goodness-of-fit Tests with Kernels
Model misspecification can create significant challenges for the impleme...
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11/06/2021
Kernel Methods for Multistage Causal Inference: Mediation Analysis and Dynamic Treatment Effects
We propose kernel ridge regression estimators for mediation analysis and...
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10/28/2021
MMD Aggregated Two-Sample Test
We propose a novel nonparametric two-sample test based on the Maximum Me...
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06/16/2021
KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support
We study the gradient flow for a relaxed approximation to the Kullback-L...
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06/15/2021
Self-Supervised Learning with Kernel Dependence Maximization
We approach self-supervised learning of image representations from a sta...
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06/07/2021
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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06/06/2021
Towards an Understanding of Benign Overfitting in Neural Networks
Modern machine learning models often employ a huge number of parameters ...
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05/21/2021
On Instrumental Variable Regression for Deep Offline Policy Evaluation
We show that the popular reinforcement learning (RL) strategy of estimat...
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05/07/2021
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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12/14/2020
A case for new neural network smoothness constraints
How sensitive should machine learning models be to input changes? We tac...
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11/17/2020
A kernel test for quasi-independence
We consider settings in which the data of interest correspond to pairs o...
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11/04/2020
Kernel Dependence Network
We propose a greedy strategy to spectrally train a deep network for mult...
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10/27/2020
A Weaker Faithfulness Assumption based on Triple Interactions
One of the core assumptions in causal discovery is the faithfulness assu...
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10/14/2020
Learning Deep Features in Instrumental Variable Regression
Instrumental variable (IV) regression is a standard strategy for learnin...
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10/12/2020
Efficient Wasserstein Natural Gradients for Reinforcement Learning
A novel optimization approach is proposed for application to policy grad...
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10/10/2020
Kernel Methods for Policy Evaluation: Treatment Effects, Mediation Analysis, and Off-Policy Planning
We propose a novel framework for non-parametric policy evaluation in sta...
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08/19/2020
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
Survival Analysis and Reliability Theory are concerned with the analysis...
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06/17/2020
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
We study the Stein Variational Gradient Descent (SVGD) algorithm, which ...
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06/15/2020
Layer-wise Learning of Kernel Dependence Networks
We propose a greedy strategy to train a deep network for multi-class cla...
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03/10/2020
KALE: When Energy-Based Learning Meets Adversarial Training
Legendre duality provides a variational lower-bound for the Kullback-Lei...
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02/21/2020
Learning Deep Kernels for Non-Parametric Two-Sample Tests
We propose a class of kernel-based two-sample tests, which aim to determ...
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12/08/2019
A kernel log-rank test of independence for right-censored data
With the incorporation of new data gathering methods in clinical researc...
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10/21/2019
Kernelized Wasserstein Natural Gradient
Many machine learning problems can be expressed as the optimization of s...
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08/20/2019
Counterfactual Distribution Regression for Structured Inference
We consider problems in which a system receives external perturbations f...
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07/01/2019
A Kernel Stein Test for Comparing Latent Variable Models
We propose a nonparametric, kernel-based test to assess the relative goo...
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06/11/2019
Maximum Mean Discrepancy Gradient Flow
We construct a Wasserstein gradient flow of the maximum mean discrepancy...
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06/01/2019
Kernel Instrumental Variable Regression
Instrumental variable regression is a strategy for learning causal relat...
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04/27/2019
Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE...
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11/20/2018
Learning deep kernels for exponential family densities
The kernel exponential family is a rich class of distributions,which can...
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11/06/2018
Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum log-likelihood estimation for exponenti...
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10/27/2018
Informative Features for Model Comparison
Given two candidate models, and a set of target observations, we address...
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10/09/2018
A maximum-mean-discrepancy goodness-of-fit test for censored data
We introduce a kernel-based goodness-of-fit test for censored data, wher...
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07/01/2018
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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05/29/2018
