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

By PulseAugur Editorial · [3 sources] ·

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

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

RANK_REASON Academic paper detailing a new statistical method for machine learning.

Read on Hugging Face Daily Papers →

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

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

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COVERAGE [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…

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