Empirical Risk Minimization of Graphical Model Parameters Given Approximate Inference, Decoding, and Model Structure

14th International Conference on Artificial Intelligence and Statistics

Veselin Stoyanov Alexander Ropson Jason Eisner

PID  :  117892

来源: CEUR

【 摘 要 】

Graphical models are often used “inappro priately,” with approximations in the topol ogy, inference, and prediction. Yet it is still common to train their parameters to approximately maximize training likelihood. We argue that instead, one should seek the parameters that minimize the empiri cal risk of the entire imperfect system. We show how to locally optimize this risk us ing backpropagation and stochastic meta descent. Over a range of syntheticdata problems, compared to the usual practice of choosing approximate MAP parameters, our approach significantly reduces loss on test