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This paper considers the problem of offline optimization, where the objective function is unknown except for a collection of “offline" data examples. While recent years have seen a flurry of work on applying various machine learning techniques to the offline optimization problem, the majority of these works focused on learning a surrogate of the unknown objective function and then applying existing optimization algorithms. While the idea of modeling the unknown objective function is intuitive and appealing, from the learning point of view it also makes it very difficult to tune the objective of the learner according to the objective of optimization. Instead of learning and then optimizing the unknown objective function, in this paper we take on a less intuitive but more direct view that optimization can be thought of as a process of sampling from a generative model. To learn an effective generative model from the offline data examples, we consider the standard technique of “re-weighting", and our main technical contribution is a probably approximately correct (PAC) lower bound on the natural optimization objective, which allows us to jointly learn a weight function and a score-based generative model from a surrogate loss function. The robustly competitive performance of the proposed approach is demonstrated via empirical studies using the standard offline optimization benchmarks. 

Current research on ferroelectric polymers centers predominantly on poly(vinylidene fluoride) (PVDF)–based fluoropolymers because of their superior performance. However, they are considered “forever chemicals” with environmental concerns. We describe a family of rationally designed fluorine-free ferroelectric polymers, featuring a polyoxypropylene main chain and disulfonyl alkyl side chains with a C3 spacer: −SO₂CH₂CHRCH₂SO₂− (R = −H or −CH₃). Both experimental and simulation results demonstrate that strong dipole-dipole interactions between neighboring disulfonyl groups induce ferroelectric ordering in the condensed state, which can be tailored by changing the R group: ferroelectric for R = −H or relaxor ferroelectric for R = −CH₃. At low electric fields, the relaxor polymer exhibits electroactuation and electrocaloric performance comparable with those of state-of-the-art PVDF-based tetrapolymers.