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Conservative Objective Models for Effective Offline Model-Based Optimization
[article]
2021
arXiv
pre-print
Computational design problems arise in a number of settings, from synthetic biology to computer architectures. In this paper, we aim to solve data-driven model-based optimization (MBO) problems, where the goal is to find a design input that maximizes an unknown objective function provided access to only a static dataset of prior experiments. Such data-driven optimization procedures are the only practical methods in many real-world domains where active data collection is expensive (e.g., when
arXiv:2107.06882v1
fatcat:xjysqud46jbczboei62usv6rdy