New framework unifies kernel embedding methods for conditional distribution comparison

By PulseAugur Editorial · [3 sources] · 2026-05-04 06:16

Researchers have introduced a unified framework called conditional maximum mean discrepancy (CMMD) to measure differences between conditional distributions. This framework encompasses various kernel-based metrics, including CMMD$_0$, CMMD$_1$, and CMMD$_2$, and offers a general level $s$ CMMD. A novel doubly robust estimator is also presented, which remains consistent if at least one of the underlying models is correctly specified. The paper demonstrates through numerical experiments that CMMD effectively identifies complex conditional dependencies for statistical testing. AI

IMPACT Introduces a new statistical method for analyzing conditional distributions, potentially improving model evaluation and testing in machine learning.

RANK_REASON Academic paper introducing a new statistical framework and estimator.

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COVERAGE [3]

Hugging Face Daily Papers TIER_1 English(EN) · 2026-05-04 06:16

Measuring Differences between Conditional Distributions using Kernel Embeddings

Comparing conditional distributions is a fundamental challenge in statistics and machine learning, with applications across a wide range of domains. While proposed methods for measuring discrepancies using kernel embeddings of distributions in a reproducing kernel Hilbert space (…
arXiv stat.ML TIER_1 English(EN) · Peter Moskvichev, Siu Lun Chau, Dino Sejdinovic · 2026-05-05 04:00

Measuring Differences between Conditional Distributions using Kernel Embeddings

arXiv:2605.02260v1 Announce Type: new Abstract: Comparing conditional distributions is a fundamental challenge in statistics and machine learning, with applications across a wide range of domains. While proposed methods for measuring discrepancies using kernel embeddings of distr…
arXiv stat.ML TIER_1 English(EN) · Dino Sejdinovic · 2026-05-04 06:16

Measuring Differences between Conditional Distributions using Kernel Embeddings

Comparing conditional distributions is a fundamental challenge in statistics and machine learning, with applications across a wide range of domains. While proposed methods for measuring discrepancies using kernel embeddings of distributions in a reproducing kernel Hilbert space (…

COVERAGE [3]

Measuring Differences between Conditional Distributions using Kernel Embeddings

Measuring Differences between Conditional Distributions using Kernel Embeddings

Measuring Differences between Conditional Distributions using Kernel Embeddings

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