[Paper] Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels

Summary

This paper introduces a novel framework designed to enhance machine-learning surrogates for scaling optimization and simulation problems. It addresses key limitations of existing methods, such as their dependence on expensive, high-quality labels and difficulties with complex optimization landscapes. By proposing a new approach, the research aims to provide more effective and inexpensive solutions for these computational challenges.

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