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A Regression Approach to Learning-Augmented Online Algorithms
Keerti Anand · Rong Ge · Amit Kumar · Debmalya Panigrahi

Thu Dec 09 04:30 PM -- 06:00 PM (PST) @

The emerging field of learning-augmented online algorithms uses ML techniques to predict future input parameters and thereby improve the performance of online algorithms. Since these parameters are, in general, real-valued functions, a natural approach is to use regression techniques to make these predictions. We introduce this approach in this paper, and explore it in the context of a general online search framework that captures classic problems like (generalized) ski rental, bin packing, minimum makespan scheduling, etc. We show nearly tight bounds on the sample complexity of this regression problem, and extend our results to the agnostic setting. From a technical standpoint, we show that the key is to incorporate online optimization benchmarks in the design of the loss function for the regression problem, thereby diverging from the use of off-the-shelf regression tools with standard bounds on statistical error.

Author Information

Keerti Anand (Duke University)
Rong Ge (Duke University)
Amit Kumar (Indian Institute of Technology Delhi)
Debmalya Panigrahi (Department of Computer Science, Duke University)

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