  We consider using an ensemble of binary classifiers for transductive
prediction, when unlabeled test data are known in advance. We derive minimax
optimal rules for confidence-rated prediction in this setting. By using
PAC-Bayes analysis on these rules, we obtain data-dependent performance
guarantees without distributional assumptions on the data. Our analysis
techniques are readily extended to a setting in which the predictor is allowed
to abstain.
