# Regret Bounds for Non-decomposable Metrics with Missing Labels

@inproceedings{Natarajan2016RegretBF, title={Regret Bounds for Non-decomposable Metrics with Missing Labels}, author={N. Natarajan and P. Jain}, booktitle={NIPS}, year={2016} }

We consider the problem of recommending relevant labels (items) for a given data point (user). In particular, we are interested in the practically important setting where the evaluation is with respect to non-decomposable (over labels) performance metrics like the $F_1$ measure, and the training data has missing labels. To this end, we propose a generic framework that given a performance metric $\Psi$, can devise a regularized objective function and a threshold such that all the values in the… CONTINUE READING

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