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New research tackles variance reduction in domain adaptation techniques · 2 sources tracked

By PulseAugur Editorial · [3 sources] ·

Two new research papers propose methods to reduce variance in domain adaptation techniques. The first paper, "Variance-reduced Domain Adaptation using Paired Sampling" (PSDA), introduces a stochastic variance reduction (SVR) technique that pairs observations within and across domains to minimize gradient variance. The second paper, "Online Variance Reduction for Domain Adaptation on Streaming Data," presents ARROW, an adaptive SVR algorithm designed for streamed data that maintains moving average references and adaptively reweights minibatches. AI

IMPACT These methods aim to improve the accuracy and efficiency of machine learning models in handling data from different distributions.

RANK_REASON Two academic papers published on arXiv proposing novel methods for domain adaptation.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New research tackles variance reduction in domain adaptation techniques · 2 sources tracked

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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Andrea Napoli ·

    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). However, high variance in these losses has been shown to undermine their effective…

  2. arXiv cs.LG TIER_1 English(EN) · Andrea Napoli ·

    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. Although various offline SVR algorithms for these losses have been propos…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Variance-reduced Domain Adaptation using Paired Sampling

    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. Further…

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