On Multilabel Classification and Ranking with Partial Feedback
Machine Learning
2013-01-17 v3
Abstract
We present a novel multilabel/ranking algorithm working in partial information settings. The algorithm is based on 2nd-order descent methods, and relies on upper-confidence bounds to trade-off exploration and exploitation. We analyze this algorithm in a partial adversarial setting, where covariates can be adversarial, but multilabel probabilities are ruled by (generalized) linear models. We show O(T^{1/2} log T) regret bounds, which improve in several ways on the existing results. We test the effectiveness of our upper-confidence scheme by contrasting against full-information baselines on real-world multilabel datasets, often obtaining comparable performance.
Cite
@article{arxiv.1207.0166,
title = {On Multilabel Classification and Ranking with Partial Feedback},
author = {Claudio Gentile and Francesco Orabona},
journal= {arXiv preprint arXiv:1207.0166},
year = {2013}
}