PulseAugur
中
实时 22:25:48

New method adds missingness to SGD to reduce bias in incomplete data

作者 PulseAugur 编辑部 · [3 个来源] ·

Researchers have developed a novel method called Richardson-SGD to address gradient bias in stochastic gradient descent when dealing with incomplete data. The technique involves deliberately introducing additional missingness to data, then combining gradients from different levels of missingness to cancel out bias. This approach is model-agnostic, computationally efficient, and has shown empirical improvements in optimization and estimation for various models, even when combined with existing imputation methods like MICE. AI

影响 Introduces a novel technique to improve the accuracy of machine learning models trained on incomplete datasets.

排序理由 Academic paper detailing a new statistical method for machine learning.

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

New method adds missingness to SGD to reduce bias in incomplete data

报道来源 [3]

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

    Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data

    Stochastic gradient methods are central to modern large-scale learning, but their use with incomplete covariates remains delicate since imputation schemes generally introduce systematic gradient biases, as shown for linear models. In this work, we prove that all parametric models…

  2. arXiv stat.ML TIER_1 English(EN) · Ferdinand Genans (SU, LPSM), Erwan Scornet (SU, LPSM) ·

    Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data

    arXiv:2605.19641v1 Announce Type: new Abstract: Stochastic gradient methods are central to modern large-scale learning, but their use with incomplete covariates remains delicate since imputation schemes generally introduce systematic gradient biases, as shown for linear models. I…

  3. arXiv stat.ML TIER_1 English(EN) · Erwan Scornet ·

    Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data

    Stochastic gradient methods are central to modern large-scale learning, but their use with incomplete covariates remains delicate since imputation schemes generally introduce systematic gradient biases, as shown for linear models. In this work, we prove that all parametric models…

相关实体

相关话题