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This work studies idealized optimizers geometries in continuous time to model small-step training in deep learning and characterize a region where majority losses are optimized regardless of the admissible minority structure.
Saddle-point problems play an important role in modern machine learning, including robust optimization and algorithmic fairness. We investigate structured convex--concave minimax problems where the primal variable lies in a high-dimensional, unconstrained space $\mathbb{R}^d$, while the dual variable is confined to a c...
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