The Greedy Miser: Learning under Test-time Budgets

Understand

As machine learning algorithms enter applications in industrial settings, there is increased interest in controlling their cpu-time during testing.

• The cpu-time consists of the running time of the algorithm and the extraction time of the features.
• The latter can vary drastically when the feature set is diverse.
• In this paper, we propose an algorithm, the Greedy Miser, that incorporates the feature extraction cost during training to explicitly minimize the cpu-time during testing.