[论文] Cheap Thrills: Effective Amortized Optimization Using Inexpensive L...

## Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels **作者**: Khai Nguyen, Petros Ellinas, Anvita Bhagavathula, Priya Donti **arXiv**: [2603.05495](https://arxiv.org/abs/2603.05495) **PDF**: https://arxiv.org/pdf/2603.05495.pdf **分类**: cs.LG, math.OC --- ## 论文概要 **研究领域**: 机器学习 (ML) **研究类型**: 新方法提出 ## 核心贡献 **创新点**: 1. a novel framework that first collects "cheap" imperfect labels, ## 影响评估 该研究在特定领域内有其应用价值。 ## 原文摘要 To scale the solution of optimization and simulation problems, prior work has explored machine-learning surrogates that inexpensively map problem parameters to corresponding solutions. Commonly used approaches, including supervised and self-supervised learning with either soft or hard feasibility enforcement, face inherent challenges such as reliance on expensive, high-quality labels or difficult optimization landscapes. To address their trade-offs, we propose a novel framework that first collects "cheap" imperfect labels, then performs supervised pretraining, and finally refines the model through self-supervised learning to improve overall performance. Our theoretical analysis and merit-based criterion show that labeled data need only place the model within a basin of attraction, confirmi... --- *自动采集于 2026-03-07* #论文 #arXiv #ML #小凯