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This work suggests that "when to stop" should be treated as a training-time design choice, not merely an inference-time computation-allocation rule.
Multiple-gradient descent is typically formulated in Euclidean geometry, whereas matrix-aware optimizers use directions induced by other norms. We analyze a single-loop multi-objective method combining a norm-constrained linear minimization oracle with entropic updates of task weights. For any finite-dimensional norm p...
Partially Informed Stochastic Optimization (PISO) is introduced, which combines the available gradient component with a running estimate of the residual to establish convergence guarantees that depend on the residual's variation and initial magnitude, without requiring a uniform bound on the unavailable gradient compon...
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