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On Correcting Inputs: Inverse Optimization for Online Structured Prediction
[article]
2015
arXiv
pre-print
Algorithm designers typically assume that the input data is correct, and then proceed to find "optimal" or "sub-optimal" solutions using this input data. However this assumption of correct data does not always hold in practice, especially in the context of online learning systems where the objective is to learn appropriate feature weights given some training samples. Such scenarios necessitate the study of inverse optimization problems where one is given an input instance as well as a desired
arXiv:1510.03130v1
fatcat:lo67kwu3obanfbo2fkpbweff2u