Learning-Augmented Moment Estimation on Time-Decay Models
Motivated by the prevalence and success of machine learning, a line of recent work has studied learning-augmented algorithms in the streaming model. These results have shown that for natural and practical oracles implemented with machine learning models, we can obtain streaming algorithms with improved space efficiency that are otherwise provably impossible. On the other hand, our understanding is much more limited when items are weighted unequally, for example, in the sliding-window model, where older data must be expunged from the dataset, e.g., by privacy regulation laws. In this paper, we utilize an oracle for the heavy-hitters of datasets to give learning-augmented algorithms for a n...
| Authors | Soham Nagawanshi, Shalini Panthangi, Chen Wang et al. |
|---|---|
| Primary category | cs.DS |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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