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stat.ML2024
An Overview of Causal Inference using Kernel Embeddings
Dino Sejdinovic
Kernel embeddings have emerged as a powerful tool for representing probability measures in a variety of statistical inference problems. By mapping probability measures into a repro…
stat.ML2024★ 1 cited
Neural-Kernel Conditional Mean Embeddings
Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic
Kernel conditional mean embeddings (CMEs) offer a powerful framework for representing conditional distribution, but they often face scalability and expressiveness challenges. In th…