Research map / Learning theory and algorithms / Prediction and decision losses
Contract Scheduling With Predictions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the adaptive Robust schedule with H = 0.1 instead of the baseline exponential schedule | Interruption-time prediction · Deadlines in [2,2^{20}] | percentage of interruptions with a longer contract: 88.71% | Helped |
| Use the adaptive Robust schedule with H = 0.1 instead of the baseline exponential schedule | Interruption-time prediction · Deadlines in [2,2^{20}] | percentage with at least 20% longer contract: 66.43% | Helped |
| Set buffer p = 0.1 rather than p = 0.05, 0.2, or 0.3 (ablation) | Adaptive Robust schedule · Interruption-time prediction, H = 0.1 | expected acceleration ratio: 2.23 compared to 2.41, 2.49 and 2.85 | Helped |
| Increase error bound H (ablation) | Adaptive Robust schedule · Interruption-time prediction | percentage of interruptions with a longer contract: 0.983 -> 0.810 | Hurt |
| Increase error bound H (ablation) | Adaptive Robust schedule · Interruption-time prediction | percentage with at least 20% longer contract: 0.776 -> 0.453 | Hurt |
| Set buffer p = 0.1 rather than p = 0.0, 0.2, or 0.3 (ablation) | Oblivious Robust schedule · Interruption-time prediction, H = 0.1 | percentage of interruptions with a longer contract: 0.703, 0.896, 0.936, 0.853, 0.750 | Helped |
| Set buffer p = 0.05 or 0.07 rather than p = 0.0 (ablation) | Oblivious Robust schedule · Binary predictions | percentage of interruptions with a longer contract: 0.623 -> 0.813; 0.876 | Helped |
| Increase query error level (ablation) | Adaptive Robust schedule · Binary predictions | percentage with at least 20% longer contract: 0.703 -> 0.610 | Hurt |
| Use binary-query Robust with p = 0.05, below the assumed H = 0.1 (ablation) | Robust (oblivious) · Binary predictions, H = 0.1 | percentage of interruptions with a longer contract: 89.81% | Helped |
Closest papers by meaning
- Non-clairvoyant Scheduling with Partial Predictions (ICML 2024 · similarity 0.84)
- Non-Clairvoyant Scheduling with Progress Bars (NeurIPS 2025 · similarity 0.82)
- SkipPredict: When to Invest in Predictions for Scheduling (NeurIPS 2024 · similarity 0.82)
- Resilient Byzantine Agreement with Predictions (NeurIPS 2026 · similarity 0.81)
- Algorithms with Calibrated Machine Learning Predictions (ICML 2025 · similarity 0.81)
- Beyond Prediction: Tail-Aware Scheduling for LLM Inference (ICML 2026 · similarity 0.81)
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