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cs.DS2026
Online Algorithms with a Sample: Tight Bounds and Adversarial Robustness
Anish Hebbar, Ravi Kumar, Roie Levin +3
Suppose an online algorithm is given an unbiased -sample of its input as offline advice; can the algorithm exploit the sample to achieve beyond-worst-case performance? We study…
cs.DS2026
Learning-Augmented Algorithms for -median via Online Learning
Anish Hebbar, Rong Ge, Amit Kumar +1
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 introd…