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MAdam optimizer improves multi-objective machine learning

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced MAdam, a novel wrapper for the Adam optimizer designed to improve multi-objective optimization in machine learning. MAdam addresses two key issues: a weighting mismatch where Adam's statistics dilute objective preferences, and a geometric mismatch where Adam's adaptive metric distorts objective alignment. By preconditioning the update direction, MAdam ensures the realized update is governed by the metric-conditioned objective, leading to consistent improvements across various applications like multi-task learning and medical imaging. AI

IMPACT Enhances multi-objective optimization techniques, potentially improving performance in complex machine learning tasks.

RANK_REASON The cluster contains a research paper detailing a new method for optimizing machine learning models.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

MAdam optimizer improves multi-objective machine learning

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 Română(RO) · Fengbei Liu, Rachit Saluja, Sunwoo Kwak, Ruibo Wang, Ruining Deng, Heejong Kim, Johannes C. Paetzold, Mert R. Sabuncu ·

    MAdam: Metric-Aware Multi-Objective Adam

    arXiv:2606.03904v1 Announce Type: new Abstract: Multi-objective optimization (MOO) underlies many machine learning problems, yet MOO solvers across the loss-balancing, gradient-balancing, and Pareto-based families almost universally hand their reconciled directions to Adam~\cite{…

  2. arXiv cs.LG TIER_1 Română(RO) · Mert R. Sabuncu ·

    MAdam: Metric-Aware Multi-Objective Adam

    Multi-objective optimization (MOO) underlies many machine learning problems, yet MOO solvers across the loss-balancing, gradient-balancing, and Pareto-based families almost universally hand their reconciled directions to Adam~\cite{kingma2015adam}. We show this coupling introduce…

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