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Domain Generalization via Invariant Feature Representation
TLDR
Domain-Invariant Component Analysis (DICA), a kernel-based optimization algorithm that learns an invariant transformation by minimizing the dissimilarity across domains, whilst preserving the functional relationship between input and output variables is proposed. Expand
Domain Adaptation under Target and Conditional Shift
TLDR
This work considers domain adaptation under three possible scenarios, kernel embedding of conditional as well as marginal distributions, and proposes to estimate the weights or transformations by reweighting or transforming training data to reproduce the covariate distribution on the test domain. Expand
Kernel Mean Embedding of Distributions: A Review and Beyonds
TLDR
A comprehensive review of existing work and recent advances in the Hilbert space embedding of distributions, and to discuss the most challenging issues and open problems that could lead to new research directions. Expand
Learning from Distributions via Support Measure Machines
TLDR
A kernel-based discriminative learning framework on probability measures that learns using a collection of probability distributions that have been constructed to meaningfully represent training data and proposes a flexible SVM (Flex-SVM) that places different kernel functions on each training example. Expand
Towards a Learning Theory of Cause-Effect Inference
TLDR
This work poses causal inference as the problem of learning to classify probability distributions, and extends the ideas to infer causal relationships between more than two variables. Expand
One-Class Support Measure Machines for Group Anomaly Detection
TLDR
It is shown that various types of VKDEs can be considered as solutions to a class of regularization problems studied in this paper, bridging the gap between large-margin methods and kernel density estimators. Expand
A Permutation-Based Kernel Conditional Independence Test
TLDR
A new kernel CI test is proposed that uses a single, learned permutation to convert the CI test problem into an easier two-sample test problem and has power competitive with state-of-the-art kernel CI tests. Expand
Kernel Mean Estimation and Stein Effect
TLDR
Focusing on a subset of this class of estimators, this work proposes efficient shrinkage estimators for the kernel mean that can be improved due to a well-known phenomenon in statistics called Stein's phenomenon. Expand
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings
TLDR
A new operator-free, measure-theoretic definition of the conditional mean embedding as a random variable taking values in a reproducing kernel Hilbert space is presented, and a thorough analysis of its properties, including universal consistency is provided. Expand
Minimax Estimation of Kernel Mean Embeddings
TLDR
The interesting aspect of this result is that the minimax rate is independent of the smoothness of the kernel and the density of $P$ (if it exists). Expand
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