AI RESEARCH
Cost-Aware Learning
arXiv CS.LG
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ArXi:2604.28020v1 Announce Type: new We consider the problem of Cost-Aware Learning, where sampling different component functions of a finite-sum objective incurs different costs. The objective is to reach a target error while minimizing the total cost. First, we propose the Cost-Aware Stochastic Gradient Descent algorithm for convex functions, and derive its cost complexity to attain an error of $\epsilon$. Furthermore, we establish a lower bound for this setting and provide a subset selection algorithm to further reduce the cost of