资源分类
》 资源 》论文》Optimization over Continuous and Multi-dimensional Decisions with Observational Data

Optimization over Continuous and Multi-dimensional Decisions with Observational Data

2020-02-14 | |  40 |   44 |   0

Abstract 

We consider the optimization of an uncertain objective over continuous and multidimensional decision spaces in problems in which we are only provided with observational data. We propose a novel algorithmic framework that is tractable, asymptotically consistent, and superior to comparable methods on example problems. Our approach leverages predictive machine learning methods and incorporates information on the uncertainty of the predicted outcomes for the purpose of prescribing decisions. We demonstrate the efficacy of our method on examples involving both synthetic and real data sets.

上一篇：Improved Algorithms for Collaborative PAC Learning

下一篇：First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...