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08/30/2023
Causal Strategic Learning with Competitive Selection
We study the problem of agent selection in causal strategic learning und...
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07/21/2023
Fast Adaptive Test-Time Defense with Robust Features
Adaptive test-time defenses are used to improve the robustness of deep n...
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05/24/2023
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models
We present a novel approach for explaining Gaussian processes (GPs) that...
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05/19/2023
A Measure-Theoretic Axiomatisation of Causality
Causality is a central concept in a wide range of research areas, yet th...
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12/13/2022
On the Relationship Between Explanation and Prediction: A Causal View
Explainability has become a central requirement for the development, dep...
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07/20/2022
Learning Counterfactually Invariant Predictors
We propose a method to learn predictors that are invariant under counter...
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07/11/2022
Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
Important problems in causal inference, economics, and, more generally, ...
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06/24/2022
Gated Domain Units for Multi-source Domain Generalization
Distribution shift (DS) is a common problem that deteriorates the perfor...
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06/17/2022
AutoML Two-Sample Test
Two-sample tests are important in statistics and machine learning, both ...
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06/05/2022
Impossibility of Collective Intelligence
Democratization of AI involves training and deploying machine learning m...
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02/09/2022
Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes
This paper provides some first steps in developing empirical process the...
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02/16/2021
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression
We propose to analyse the conditional distributional treatment effect (C...
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02/10/2021
An Optimal Witness Function for Two-Sample Testing
We propose data-dependent test statistics based on a one-dimensional wit...
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10/21/2020
Regularised Least-Squares Regression with Infinite-Dimensional Output Space
We present some learning theory results on reproducing kernel Hilbert sp...
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08/10/2020
Grasping Field: Learning Implicit Representations for Human Grasps
In recent years, substantial progress has been made on robotic grasping ...
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06/03/2020
Learning Kernel Tests Without Data Splitting
Modern large-scale kernel-based tests such as maximum mean discrepancy (...
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02/21/2020
Kernel Conditional Moment Test via Maximum Moment Restriction
We propose a new family of specification tests called kernel conditional...
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02/10/2020
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings
We present a new operator-free, measure-theoretic definition of the cond...
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11/25/2019
A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming
This work presents the concept of kernel mean embedding and kernel proba...
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10/29/2019
Kernel-Guided Training of Implicit Generative Models with Stability Guarantees
Modern implicit generative models such as generative adversarial network...
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10/27/2019
Dual IV: A Single Stage Instrumental Variable Regression
We present a novel single-stage procedure for instrumental variable (IV)...
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06/03/2019
Low-rank Random Tensor for Bilinear Pooling
Bilinear pooling is capable of extracting high-order information from da...
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05/31/2019
Quantum Mean Embedding of Probability Distributions
The kernel mean embedding of probability distributions is commonly used ...
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05/29/2019
Private Causal Inference using Propensity Scores
The use of propensity score methods to reduce selection bias when determ...
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05/27/2019
Kernel Conditional Density Operators
We introduce a conditional density estimation model termed the condition...
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02/08/2019
Improving Consequential Decision Making under Imperfect Predictions
Consequential decisions are increasingly informed by sophisticated data-...
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01/26/2019
Witnessing Adversarial Training in Reproducing Kernel Hilbert Spaces
Modern implicit generative models such as generative adversarial network...
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12/05/2018
Local Temporal Bilinear Pooling for Fine-grained Action Parsing
Fine-grained temporal action parsing is important in many applications, ...
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05/22/2018
Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference
This paper introduces a novel Hilbert space representation of a counterf...
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12/05/2017
Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces
Transfer operators such as the Perron-Frobenius or Koopman operator play...
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08/31/2017
Design and Analysis of the NIPS 2016 Review Process
Neural Information Processing Systems (NIPS) is a top-tier annual confer...
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05/31/2016
Kernel Mean Embedding of Distributions: A Review and Beyond
A Hilbert space embedding of a distribution---in short, a kernel mean em...
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02/09/2015
Towards a Learning Theory of Cause-Effect Inference
We pose causal inference as the problem of learning to classify probabil...
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01/27/2015
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations
We describe a method to perform functional operations on probability dis...
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11/04/2014
Kernel Mean Estimation via Spectral Filtering
The problem of estimating the kernel mean in a reproducing kernel Hilber...
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08/09/2014
One-Class Support Measure Machines for Group Anomaly Detection
We propose one-class support measure machines (OCSMMs) for group anomaly...
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05/21/2014
Kernel Mean Shrinkage Estimators
A mean function in a reproducing kernel Hilbert space (RKHS), or a kerne...
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06/04/2013
Kernel Mean Estimation and Stein's Effect
A mean function in reproducing kernel Hilbert space, or a kernel mean, i...
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01/10/2013
Domain Generalization via Invariant Feature Representation
This paper investigates domain generalization: How to take knowledge acq...
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10/16/2012
Hilbert Space Embedding for Dirichlet Process Mixtures
This paper proposes a Hilbert space embedding for Dirichlet Process mixt...
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02/29/2012