Learning-Augmented Algorithms for $k$-median via Online Learning
The field of learning-augmented algorithms seeks to use ML techniques on past instances of a problem to inform an algorithm designed for a future instance. In this paper, we introduce a novel model for learning-augmented algorithms inspired by online learning. In this model, we are given a sequence of instances of a problem and the goal of the learning-augmented algorithm is to use prior instances to propose a solution to a future instance of the problem. The performance of the algorithm is measured by its average performance across all the instances, where the performance on a single instance is the ratio between the cost of the algorithm's solution and that of an optimal solution for th...
| Authors | Anish Hebbar, Rong Ge, Amit Kumar et al. |
|---|---|
| Primary category | cs.DS |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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