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Adaptive Classification for Prediction Under a Budget
Feng Nan · Venkatesh Saligrama

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #25

We propose a novel adaptive approximation approach for test-time resource-constrained prediction motivated by Mobile, IoT, health, security and other applications, where constraints in the form of computation, communication, latency and feature acquisition costs arise. We learn an adaptive low-cost system by training a gating and prediction model that limits utilization of a high-cost model to hard input instances and gates easy-to-handle input instances to a low-cost model. Our method is based on adaptively approximating the high-cost model in regions where low-cost models suffice for making highly accurate predictions. We pose an empirical loss minimization problem with cost constraints to jointly train gating and prediction models. On a number of benchmark datasets our method outperforms state-of-the-art achieving higher accuracy for the same cost.

Author Information

Feng Nan (Boston University)
Venkatesh Saligrama (Boston University)

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