Hugging Face Trending Papers July 22, 2026

Variance-reduced Domain Adaptation using Paired Sampling

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Correlation alignment and the maximum mean discrepancy are two widely used distribution-matching frameworks for unsupervised domain adaptation (UDA). However, high variance in these losses has been shown to undermine their effectiveness in minibatch optimisation settings.

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arXiv Machine Learning

Jul 23

Variance-reduced Domain Adaptation using Paired Sampling

arXiv:2607. 20367v1 Announce Type: new Abstract: Correlation alignment and the maximum mean discrepancy are two widely used distribution-matching frameworks for unsupervised domain adaptation (UDA).

By Andrea Napoli

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arXiv Machine Learning

Jul 23

Online Variance Reduction for Domain Adaptation on Streaming Data

arXiv:2607. 20374v1 Announce Type: new Abstract: This paper studies the problem of stochastic variance reduction (SVR) for the maximum mean discrepancy (MMD) and correlation alignment (CORAL) loss functions.

By Andrea Napoli

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arXiv AI

Aug 5

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arXiv:2407. 21311v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data.

By Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah

llms computer-vision fine-tuning safety

arXiv AI

Jun 10

MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance

arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.

By Matina Mahdizadeh Sani, Nima Jamali, Mohammad Jalali, Farzan Farnia

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arXiv Machine Learning

Jun 2

Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain

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By Yuan Yao, Jin Song, Huixia Li, Tongtong Yuan, Jiaqi Wu, Yu Zhang

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arXiv AI

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By Yiheng Xiong, Luisa Gall\'ee, Daniel Santak Wolf, Heiko Hillenhagen, Michael G\"otz

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