Research map / Bandits and online learning / Online convex optimization and games
Learning-to-learn non-convex piecewise-Lipschitz functions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Meta-learn the exponential forecaster initialization from related tasks | Exponential forecaster · Omniglot one-shot | performance (% of offline optimum): 88.67±0.47% -> 89.65±0.49% | Helped |
| Meta-learn the exponential forecaster initialization from related tasks | Exponential forecaster · Omniglot five-shot | performance (% of offline optimum): 95.02±0.19% -> 96.05±0.15% | Helped |
| Meta-learn the exponential forecaster initialization from related tasks | Exponential forecaster · Gaussian Mixture one-shot | performance (% of offline optimum): 90.10±1.10% -> 95.76±0.60% | Helped |
| Meta-learn the exponential forecaster initialization from related tasks | Exponential forecaster · Gaussian Mixture five-shot | performance (% of offline optimum): 91.43±0.44% -> 96.39±0.27% | Helped |
| Meta-learn the exponential forecaster initialization from related tasks | Exponential forecaster · Knapsack one-shot | performance (% of offline optimum): 84.74±0.29% -> 85.66±0.57% | Helped |
| Meta-learn the exponential forecaster initialization from related tasks | Exponential forecaster · Knapsack five-shot | performance (% of offline optimum): 98.89±0.17% -> 99.12±0.15% | Helped |
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