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{kingma2015adam}. We show this coupling introduces two systematic gaps between the solver's intent and the optimizer's execution. The first is a \emph{weighting mismatch}: Adam's second-moment denominator entangles the time-varying preference vector with gradient statistics, marginalizing the preference into a history a
This research addresses fundamental optimization challenges in multi-objective machine learning, a field growing in complexity and application across various AI domains.
Improving multi-objective optimization directly enhances the efficiency, reliability, and interpretability of complex AI systems, impacting their development and deployment.
New optimization approaches will enable more robust and precisely controlled training of AI models, particularly those balancing multiple, often conflicting, objectives.
- · AI researchers and developers
- · Companies deploying complex AI models
- · Advanced AI applications (e.g., autonomous systems)
- · Inefficient AI training practices
- · Systems highly reliant on manual hyperparameter tuning
More stable and faster convergence for multi-objective optimization problems in deep learning.
Accelerated development of AI models that can better balance competing performance metrics or ethical considerations.
Potentially enables new classes of AI applications that require precise control over multiple output dimensions or system behaviors.
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Read at arXiv cs.LG