Research map / Bandits and online learning / Online convex optimization and games
Parsimonious Learning-Augmented Caching
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use AdaptiveQuery-2 instead of RandomMarker | CitiBike caching with mean predictions · Mean Predictions | competitive ratio: 3.14 -> 2.91 | Helped |
| Increase the query budget from 2 to 8 pages per cache miss | AdaptiveQuery · CitiBike, synthetic predictions, σ = 0 | competitive ratio: 2.46 -> 1.86 | Helped |
| Increase the query budget from 2 to 8 pages per cache miss | AdaptiveQuery · CitiBike, synthetic predictions, σ = 6 | competitive ratio: 2.65 -> 2.54 | No clear effect |
| Use AdaptiveQuery-8 rather than LVMarker | CitiBike caching · Synthetic predictions, σ = 6 | competitive ratio: 3.11 -> 2.54 | Helped |
| Use AdaptiveQuery-8 rather than BlindOracle | CitiBike caching · Synthetic predictions, σ = 4 | competitive ratio: 3.92 -> 2.07 | Helped |
| Query b unmarked pages uniformly and evict the one predicted furthest in the future | AdaptiveQuery-b · Caching with error-free predictions | competitive ratio | Helped |
| Switch to uniform random evictions after chain length exceeds log k | AdaptiveQuery-b · Caching with arbitrary prediction error | competitive ratio | Helped |
Closest papers by meaning
- Algorithms for Caching and MTS with reduced number of predictions (ICLR 2024 · similarity 0.92)
- Robustifying Learning-Augmented Caching Efficiently without Compromising 1-Consistency (NeurIPS 2025 · similarity 0.89)
- Learning-Augmented Algorithms with Explicit Predictors (NeurIPS 2024 · similarity 0.89)
- Overcoming Brittleness in Pareto-Optimal Learning Augmented Algorithms (NeurIPS 2024 · similarity 0.86)
- Learning Augmented Exact Exponential Algorithms (NeurIPS 2026 · similarity 0.85)
- Parsimonious Learning-Augmented Approximations for Dense Instances of $\mathcal{NP}$-hard Problems (ICML 2024 · similarity 0.85)
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