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info.json
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info.json
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{
"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 diverse real- world multilabel data sets, often obtaining comparable performance.",
"authors": [
"Claudio Gentile",
"Francesco Orabona"
],
"id": "gentile14a",
"issue": 70,
"pages": [
2451,
2487
],
"title": "On Multilabel Classification and Ranking with Bandit Feedback",
"volume": 15,
"year": 2014
}