5 problems
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The minimum-attention conjecture for meta-learning adaptation
Minimum attention is defined by the control-change functional … Here is a control varying over state space and time interval , and the functional penalizes chan…
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Conjecture explaining the difference between fixed and dynamically learned optimizer coefficients
Let \texttt{MADA}\xspace\ be a meta-adaptive optimizer that learns interpolation coefficients, and let \texttt{MADA}\xspace\-FS denote the version using the fixed final optimiz…
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The task-diversity explanation for greater meta-training variance
Task-diversity conjecture. We conjecture that this alternation is caused by the greater variance in meta-training arising from the higher diversity in tasks.
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Optimal sample complexity of BSGD for strongly and weakly convex objectives
Let BSGD denote the biased stochastic first-order algorithm proposed for conditional stochastic optimization. For strongly convex and weakly convex objectives, the BSGD optimality…
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Conjecture on learning-algorithm selection in surrogate-based optimization
A surrogate-based optimization framework uses a surrogate model for the true objective function and a model of a substitution strategy, called a relevator, to decide whether a cand…