Compressing Value Predictions for Learning-Augmented Metrical Task Systems
Learning-augmented algorithms for metrical task systems (MTS) can exploit predictions of canonical dual values, but existing formulations typically require a prediction for every state. We study whether these predictions can be compressed to a small set of representative states while retaining their algorithmic value. We introduce landmark-compressed value predictions, in which the predictor reports predicted dual values only at $m$ landmarks and the remaining values are reconstructed by a Lipschitz extension. Our algorithm achieves additive excess cost $O(T\,r(L) + \sum_t δ_t)$, where $r(L)$ is the covering radius of the landmarks and $δ_t$ measures prediction error up to additive shifts...
| Authors | Sizhe Li, Yecheng Li, Kun He |
|---|---|
| Primary category | cs.LG |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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