  As machine learning transitions increasingly towards real world applications
controlling the test-time cost of algorithms becomes more and more crucial.
Recent work, such as the Greedy Miser and Speedboost, incorporate test-time
budget constraints into the training procedure and learn classifiers that
provably stay within budget (in expectation). However, so far, these algorithms
are limited to the supervised learning scenario where sufficient amounts of
labeled data are available. In this paper we investigate the common scenario
where labeled data is scarce but unlabeled data is available in abundance. We
propose an algorithm that leverages the unlabeled data (through Laplace
smoothing) and learns classifiers with budget constraints. Our model, based on
gradient boosted regression trees (GBRT), is, to our knowledge, the first
algorithm for semi-supervised budgeted learning.
