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Evidence record 16975 · automatically gathered

Non-asymptotic implicit bias of logistic regression at early-stage gradient descent dynamics

Gradient descent has been of particular interest in modern machine learning beyond sole focus on optimization. Implicit bias emerging from optimization, though not being encoded by the learning objective, often prevents from overfitting to spurious patterns. A typical instance is the max-margin implicit bias of a linear classifier, widely established for exponentially tailed loss functions. Even after having a given dataset separated, the parameter vector continues to evolve towards the max-marg

Record details

Published: 5 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Bias & fairness
Retrieved: 6 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (5 August 2026), “Non-asymptotic implicit bias of logistic regression at early-stage gradient descent dynamics,” evidence record 16975, https://ethics.ai/record/16975 (originally published by arXiv cs.LG).

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