document: The offset tree for learning with partial labels We present an algorithm , called the Offset Tree , for learning to make decisions in situations where the payoff of only one choice is observed , rather than all choices . The algorithm reduces this setting to binary classification , allowing one to reuse any existing , fully supervised binary classification algorithm in this partial information setting . We show that the Offset Tree is an optimal reduction to binary classification . In particular , it has regret at most ( k-1 ) times the regret of the binary classifier it uses ( where k is the number of choices ) , and no reduction to binary classification can do better . This reduction is also computationally optimal , both at training and test time , requiring just O ( log2 k ) work to train on an example or make a prediction . Experiments with the Offset Tree show that it generally performs better than several alternative approaches .
doc_bio_tags: O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O