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Evidence record 3077 · automatically gathered

Dangerous Liaisons of Convex Learning and Non-Affine Aggregation

Last-iterate convergence and generalization guarantees in first-order convex learning hinge on the monotonicity of the update operator. While linear averaging preserves the monotonicity of gradient updates, this property is often violated when gradients are aggregated non-affinely, as in modern pipelines enforcing constraints like adaptivity, privacy, robustness or fairness. Whether it is possible to design non-affine aggregation rules that maintain monotonicity has remained an open question. We

Record details

Published: 26 June 2026
Source: arXiv fairness query
Category: Research
Topics: Bias & fairness · Privacy
Retrieved: 14 July 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 (26 June 2026), “Dangerous Liaisons of Convex Learning and Non-Affine Aggregation,” evidence record 3077, https://ethics.ai/record/3077 (originally published by arXiv fairness query).

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