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We introduce new online and batch algorithms that are robust to data withmissing features, a situation that arises in many practical applications. Inthe online setup, we allow for the comparison hypothesis to change as afunction of the subset of features that is observed on any given round,extending the standard setting where the comparison hypothesis is fixedthroughout. In the batch setup, we present a convex relation of a non-convexproblem to jointly estimate an imputation function, used to fill in the valuesof missing features, along with the classification hypothesis. We prove regretbounds in the online setting and Rademacher complexity bounds for the batchi.i.d. setting. The algorithms are tested on several UCI datasets, showingsuperior performance over baselines.

Online and Batch Learning Algorithms for Data with Missing Features